{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi step model (encoder-decoder with teacher forcing)\n",
    "\n",
    "In this notebook, we will demonstrate how to implement a RNN model to predict multiple time steps into the future using an encoder decoder. The decoder part of the model uses teacher forcing, a method by which the output of one time step is fed to the input of the next time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from collections import UserDict\n",
    "from IPython.display import Image\n",
    "%matplotlib inline\n",
    "\n",
    "from common.utils import load_data, mape, TimeSeriesTensor, create_evaluation_df\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load data into Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "      <td>32.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load  temp\n",
       "2012-01-01 00:00:00 2,698.00 32.00\n",
       "2012-01-01 01:00:00 2,558.00 32.67\n",
       "2012-01-01 02:00:00 2,444.00 30.00\n",
       "2012-01-01 03:00:00 2,402.00 31.00\n",
       "2012-01-01 04:00:00 2,403.00 32.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = '../data/'\n",
    "energy = load_data(data_dir)\n",
    "energy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "valid_start_dt = '2014-09-01 00:00:00'\n",
    "test_start_dt = '2014-11-01 00:00:00'\n",
    "\n",
    "T = 6\n",
    "HORIZON = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create training set containing only the model features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = energy.copy()[energy.index < valid_start_dt][['load']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "y_scaler = MinMaxScaler()\n",
    "y_scaler.fit(train[['load']])\n",
    "\n",
    "X_scaler = MinMaxScaler()\n",
    "train[['load']] = X_scaler.fit_transform(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the TimeSeriesTensor convenience class to:\n",
    "1. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n",
    "2. Discard any samples with missing values\n",
    "3. Transform this Pandas dataframe into a numpy array of shape (samples, time steps, features) for input into Keras\n",
    "\n",
    "The class takes the following parameters:\n",
    "\n",
    "- **dataset**: original time series\n",
    "- **H**: the forecast horizon\n",
    "- **tensor_structure**: a dictionary discribing the tensor structure in the form { 'tensor_name' : (range(max_backward_shift, max_forward_shift), [feature, feature, ...] ) }\n",
    "- **freq**: time series frequency\n",
    "- **drop_incomplete**: (Boolean) whether to drop incomplete samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>tensor</th>\n",
       "      <th colspan=\"3\" halign=\"left\">target</th>\n",
       "      <th colspan=\"6\" halign=\"left\">encoder_input</th>\n",
       "      <th colspan=\"3\" halign=\"left\">decoder_input</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th colspan=\"3\" halign=\"left\">y</th>\n",
       "      <th colspan=\"6\" halign=\"left\">load</th>\n",
       "      <th colspan=\"3\" halign=\"left\">load</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time step</th>\n",
       "      <th>t+1</th>\n",
       "      <th>t+2</th>\n",
       "      <th>t+3</th>\n",
       "      <th>t-5</th>\n",
       "      <th>t-4</th>\n",
       "      <th>t-3</th>\n",
       "      <th>t-2</th>\n",
       "      <th>t-1</th>\n",
       "      <th>t</th>\n",
       "      <th>t</th>\n",
       "      <th>t+1</th>\n",
       "      <th>t+2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "tensor              target           encoder_input                           \\\n",
       "feature                  y                    load                            \n",
       "time step              t+1  t+2  t+3           t-5  t-4  t-3  t-2  t-1    t   \n",
       "2012-01-01 05:00:00   0.18 0.23 0.29          0.22 0.18 0.14 0.13 0.13 0.15   \n",
       "2012-01-01 06:00:00   0.23 0.29 0.35          0.18 0.14 0.13 0.13 0.15 0.18   \n",
       "2012-01-01 07:00:00   0.29 0.35 0.37          0.14 0.13 0.13 0.15 0.18 0.23   \n",
       "2012-01-01 08:00:00   0.35 0.37 0.37          0.13 0.13 0.15 0.18 0.23 0.29   \n",
       "2012-01-01 09:00:00   0.37 0.37 0.37          0.13 0.15 0.18 0.23 0.29 0.35   \n",
       "\n",
       "tensor              decoder_input            \n",
       "feature                      load            \n",
       "time step                       t  t+1  t+2  \n",
       "2012-01-01 05:00:00          0.15 0.18 0.23  \n",
       "2012-01-01 06:00:00          0.18 0.23 0.29  \n",
       "2012-01-01 07:00:00          0.23 0.29 0.35  \n",
       "2012-01-01 08:00:00          0.29 0.35 0.37  \n",
       "2012-01-01 09:00:00          0.35 0.37 0.37  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tensor_structure = {'encoder_input':(range(-T+1, 1), ['load']), 'decoder_input':(range(0, HORIZON), ['load'])}\n",
    "train_inputs = TimeSeriesTensor(train, 'load', HORIZON, tensor_structure)\n",
    "train_inputs.dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load']]\n",
    "valid[['load']] = X_scaler.transform(valid)\n",
    "valid_inputs = TimeSeriesTensor(valid, 'load', HORIZON, tensor_structure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement training model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will implement a RNN forecasting model with the following structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9AzbMD6a7JHFJEhSvPIJIkgRFl9bUVirflkkecTTMWKLbtj9JkojR5elPkiQi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\nGagsX770S7Vx6V1mPbLendt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dz4T2OFcSdNxzRUGK+TPjBVbmyneZNUufFo4wDjWlrnYkkWM6izieix2EDQMeI7nXwX5AsK/0Rm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\nPn/50Jeg+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q2v4g/tErQosm5664kAedUeZFs5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\nXIZCodPz8vJMZmbm/9l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RUB7yUXeucUkIIYQQ\nQgghhBBCCCFBQGhoaCOXy7UB8kDXmWBCSAVwOBwnOZ3Ob6Bdbrf7NhNMCCkDxnHppeOSEEIIIYQQ\nQgghhBBCggQ6LgmpfOi4JKTi0HFJCCGEEEIIIYQQQgghQQYdl4RUPnRcElJxkHfaol46ACXScUkI\nIYQQQgghhBBCCCFBAB2XhFQ+ISEh9Z1OZy/kqfFhYWFXmWBCCCGEEEIIIYQQQgghhBBCCCGEEEII\nIYQQQgghhBBCCCGEEEIIKQMxMTG1W7duXadhw4bH4mstXyghhBBCCCGEEEIIIYQQQggpNZGRkce4\n3e47HA5HE3w+1wQTQspAaGhoS5fLtdPpdI5GfrrQBBNCCCGEEEIIIYQQQgipqYSHh9/qcDjWyDqX\nbrf7bhNMCKkAssYl8tQ4aA/yl9sEE0LKgNPpbI489Bvy0Ag6LgkhhBBCCCGEEEIIISQIcLvdNzud\nzqWQOC/vNMGEqBYtWpzscDgegl3c07p16/ommJQCOi4JqTh0XBJCCCGEEEIIIYQQQkiQQcclKQ6H\nw3E97GIRNAW61ASTUkDHJbGjcePGJ4vMV3IIkH+aQltR/iRFRERcYIIJIYQQQgghhBBCCCGE1FSM\n43K5y+Va63A47jLB5AgjayLiHpyB+3GebBFUy7en7LRq1eo4OY8I5z3RBJcZOi7LT+PGjeshzTpC\nQ3E/2SGASB6vC3vYBq0zQYQQQgghhBBCCCGEEELEkRESEnJmRETE6Q0bNjzWBJeHWk2aNDkN5znL\nOEfK7WghJNhxuVxXQJnIT9rpdC4Qx5fZVWZwfDOcKw/SUIwJLhZxqMi0sFIuSJ4Wx6eE49jrcC46\nLkl1pFajRo1OcDgcJyFPHS/ffcFlR+pJqePkXK1bt65jgsuMcVzK6MHvTBAhhBBCCCGEEEIIIYQQ\np9PZPywsTBwaW6FyjwSShlyc60dstdvtHigOD7PLFq11LWlAlng47tSWLVueEBMTU9vsJiSoQV48\noo5LccYg396NuPNQHhxAvC3QLCgT339HeBbUDt9lOxmi45JUG0z9tFHqJ9hwamho6DlmV5lBfpD8\n5JW8ie3rOOcxZleJGCdngcMUx9FxWUbyncYy9TKfFwghhBBCCCGEEEIIqaE4nc4v3G63NMBuwvYO\nE1xmTMPwOtOYO6A4x6VxVH4I7cPv/YK42XIc9Bs+i9OzkzRKmuiEBCXIC0fMcWmmMe0tDktsXzXB\nfkiexL7BTZs2lXOMxmc6LsuAOLccDsftKN9aIP2uM8HkCJFfP4WHh4v9zsB9ONvsKjM4NhLn2GM6\n/Lwi99bs8gP56mTsbwltgrz4/V3QbtjBDhzbDWFn4PsWiI7LUoLnh8eRXtLJar5MXW2CCSGEEEII\nIYQQQgghNQnnEXRc4vxNsG8f1BNfA6bqk1GYOMeP2L9YGn1NMDmC4L5dgPTPhg7iXjxugskRBul/\nxByXDofjeVMGvGeCbMF5bkGcpdhyxGUZkel2kXbirJLRrFEmmBwhjrTjEnkqUvbjN6chD8vUtH40\na9ZMpl3+U/IdtmtMMDkEzpDGjyC9NrhczrRmrieuNsGEEEIIIYQQQgghhJCahPMIOS6xX6bF+xVK\nhy5FvNOLSo5xOByvYb84WVraNQiTw0tLd6Oz3Y7QOW6X4/cwx5MPmWByhIH9HzHHJcK6QgeQ/581\nQbbICCeci2tcloP+rRvUcTmdiVY6O51tTDA5QhxJxyXCH0C4zB4Qb4JsMSMypYNIcDsuWy+po96a\ne5pqO/diFZV6jWqX8bBqn/q8apf+uYryfKXapo3HdqlqP3/VOS9+vfGxsBb77498+e+TXhm9QUXP\nm4t9Q1VU+teI/w7UWrXPuFe9Nvtq9UrGRdh3qorxVGTtbkIIIYQQQgghhBBCyJHGeYQcl9gXDv2J\nfb9h+yO0vhitQ5xV2HaMiYlhg2Nl0Sb7ZBU95wKrYfiNeber6IxwFeV5W7VN76vapQ+CMqC1taLS\n1tV/beI/J74yLu+YNjM3SZiK9kxT7dIGqai0L1QbT0fVPq2Z6jD/Dhx7lXpt3vnWuUmlgjxQ4LjE\ndn5FnPgy+gvnKNZxibz2kjh0sP3EBNmC/U/ieJmmcSI+03EptFlXT7XJPEu9nnGZauu5UXVIfRj5\n5CXkm1jVFnmmXXqK5KHaUXO+u7nFu785XW7vVa1it6t281Yj/32r2nmSsBXnTCy2z6nozMY43/X4\nfKlq7zlTtZlWboc1+Zcj6bjEfukIoPFbj5kgW3CcdOYJnqlixYHYYf7ZKjr1ZuSJKGgu7N+rOi3R\nquMCrdrP1QgrXtHz9JkvD9UPNm2t727eXp/42niEFXdMhu98HbO0dX4Ji0qfh3rvJRWd/h/18syz\nVeQodowihBBCCCGEEEIIIaQq4izkuIQamOAyExERcbw0DBfnuHzqqafOwX5Zx3L6888/f5IJJpWN\njDCJ9lypotKaqaj0Ufj+j3prqVYds7VqP0+r6IwiDbwVkJyrw3yt3sC55TeiPH9BQ1WHzAjVPvNq\naxQNKRfIJ5bj0jgU90jewve12H5fRq2BcnGs5EsvFOC4fOyxx05H+FeItw9qJ3nZ7LIQx4yMIsO+\ndHzej7hj8Dm4HJcxnhOtEWFtPf9FvoqD/a9Qnb7R6s2FyAOHdrrUjkrTF7QeoG9+5gN91svJyDuZ\ntvEKZDldcN43F/nyl4w4k44G0XPvVm08F6roBQHTj5LiKeq4hI2fZXaVGdSXEThHSY7Ltgj34ree\nx9eAKdHzMdeUB9XMEZcxq+rCZi9VbdP+h+236g2TV0pbB0UZtS0sHBuFeiwK+UM+54fnxxXZnauw\nrHpL8tZixPcsVO0znrPqTLleQgghhBBCCCGEEELI0cfpdH7h8jk1/sHnadBgaEhZhGOTsR2O7V+m\nYdh2jUvEeQY6iP2DsL3IBPshDs6wsLBrY2JiapsgUhySRuLEaJfmVO08U1W7dK/VOHwop0i+bBuG\nS1BpG4ZF4ngRh0v7+XLMBNU+43HrWmM072spQB4p7Ljc7nA4+uN7H3zuW0b1gmbh2GIdl/lgXwji\nfuN2u/dimwvJtLDi+PwZegfXchO2k3Etw7C1zb81BhnpGL3gShXleUNFpe2y8pU4/u1svajy80lp\n81bh+Hbn81OhzgJR6dtwbZ0spwtHZpZIEcflanx+E3b8Mj6/UhbhuJewHYDt/uIclzjvSdBmxPk7\nIiLiShPsh5kmVkYue6Ga47h8Y8JJyDMPQpOszjJiq7Z2DOXnD3w+7g2PPv3dNH3RR3P0f+Jm6Yf7\nTdfPjJyi206epOPmjdUDl43RA5YGSsLj54/Vb8+agPiT9eMDplvHy3lOeydN1+3o+fd3SspfUl/J\nyMyo9OmoSx9VrWefYv4RIYQQQgghhBBCCCHkSOM0jktpZIVGQj0gcXaUWji+p8Ph6IfPf5bkuBQQ\nfgHijZK4iPcHtsuhmdAP+H4A27Vut/spThNbDK96TlRRqQ+o6MwRBVPh2TXEivIbbKFjO3j0qe+k\n6fM/nKOv6jxbP9B3hm46dKp+ZcIk/Un6eN1via8R+CsbDVo+Rn/iGa9fmzhJNxs2Vd/fZ4a+tsts\nnCtVn/J2mj6mvfyGjIQxv2l3LeJwkRFk0pAdnf6Faju3gTXNJrEFeeGIrXF5OGjVqtVxIvO1etBe\n1sTLfFrJ6EbLUYn8ZWvLUCFnyPFvevSZ76XqSz72OV2cg6fpZ0dN0W9On6i7ZfmcLnb5SiT5Lnrq\nJP2/sZP1EwOn61u7zdSXfjLHOp+ct/Dv2F6HqMM83wiy6PTFuPanrf9B/Mh3XJrZBaSuW49tmUcw\nyzFQDuQ15wpwXAoyahl14gjsl04AG7GNx/dobLtAkxD2I46/Gdtt8tkcVj2JHCUjKx9EnZTq6zhT\nTL4RO8b2lLfS9JWfz9GupGm6e/ZYPWtTis7ISdFzNqfomRtH6Ok/VVxyntQtKToT55XPCfPHWvny\nGtRb4tAsyFdFr1EkIzJlNHV0+jLVAf+La2QSQgghhBBCCCGEEHJkcR6hNS4PB9I4LGrdunUdE1Qz\nkVGKHdIaqPaZE6zp7YprGDaNsSd08ljOj0ZfTtMxqRP0xB9G6nnbUnTalhQ9e9MIPcOmobesknPM\n3uQ7p5x7PH7jndkT9KNfTteXfTZHn9jJNA4X53DJH+ESnT5atUm/VWld7JSKwQjyULVzXIoDJyws\n7Cr8Xn98luvuhOCqfV9fm3W+auf5yMpTJY0OQ76qhe05H6RazskXxk7WvReN0fNyh+v0rZXvdJHz\nZeC8Hkicm8+MnKpvTpilz34vVdeO9l2P7XVaHQSszgx5KjojwRrlTIqOuJyOfHW62VVmcHwYVOxU\nsXaEhoZeKTJfawYyLXnb9A4o4/+yOqUUtUVT/ouj8q4eM/U7syboyetHWE7KysorZdUM/K7kqVkb\nU6zruTVxpj717RLqKhllHZV+QLVPb69em32G+eeEEEIIIYQQQgghhJDDibOaOS5DQ0Mvw/lH4Lf+\nxvYfaCv0h/wHhA030WoGb8w7SUWnP6vaeTarNxZpbP0bVaWhVRyVb3osZ8rL4yfrMWtH6rm5w/Ws\nTfYNt0dC8tuZuSl69NpRuvW4yfr62Nm6/htm9Jhd47A4WqIyflHtMlpYI0pJtXFcIt/XxTmfhhZB\nv+O3hiAvJmC7C+qIKFXTcdlhbgMV5ZlX7PSvxlZl+so7eszUH6aOh00PtxyKdjZ/JCSdBWQU2Wxc\nw0dp4/WtibP0aeJ0ESemrSPTTCsb5fkG3+9XrZfU7A4eJVDEcTkDNnq22VVmcGwkzlEmx2VZkevF\n79yN88u06vlTNu/D9yXY9pd9h+N3D4k1Pfmc62FPQ6z1V4tOS27yzWnINzKicsjK0Va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EIIIYQQQggh1QOn09mpefPmGttPTRAhJFiJXnC6auf5Sr31jX/DZ5Rv3cu7e87Ug1eMskZ2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\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oheP6qmX39tezb0g9nF0/Tw3ofpM2Lyskx8ixH21K76xWjqJxmZGgb+TduHxpkV4W4KYb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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('../images/encoder_decoder_teacher_forcing.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model, Sequential\n",
    "from keras.layers import GRU, Dense, RepeatVector, TimeDistributed, Flatten, Input\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 32\n",
    "LATENT_DIM = 5\n",
    "EPOCHS = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# define training encoder\n",
    "encoder_input = Input(shape=(None, 1))\n",
    "encoder = GRU(LATENT_DIM, return_state=True)\n",
    "encoder_output, state_h = encoder(encoder_input)\n",
    "encoder_states = [state_h]\n",
    "\n",
    "# define training decoder\n",
    "decoder_input = Input(shape=(None, 1))\n",
    "decoder_GRU = GRU(LATENT_DIM, return_state=True, return_sequences=True)\n",
    "decoder_output, _ = decoder_GRU(decoder_input, initial_state=encoder_states)\n",
    "decoder_dense = TimeDistributed(Dense(1))\n",
    "decoder_output = decoder_dense(decoder_output)\n",
    "\n",
    "model = Model([encoder_input, decoder_input], decoder_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='RMSprop', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_target = train_inputs['target'].reshape(train_inputs['target'].shape[0], train_inputs['target'].shape[1], 1)\n",
    "valid_target = valid_inputs['target'].reshape(valid_inputs['target'].shape[0], valid_inputs['target'].shape[1], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 23368 samples, validate on 1461 samples\n",
      "Epoch 1/50\n",
      "23368/23368 [==============================] - 10s 411us/step - loss: 0.0252 - val_loss: 0.0029\n",
      "Epoch 2/50\n",
      "23368/23368 [==============================] - 9s 378us/step - loss: 0.0018 - val_loss: 0.0021\n",
      "Epoch 3/50\n",
      "23368/23368 [==============================] - 9s 388us/step - loss: 0.0011 - val_loss: 8.3251e-04\n",
      "Epoch 4/50\n",
      "23368/23368 [==============================] - 9s 379us/step - loss: 9.1167e-04 - val_loss: 9.0714e-04\n",
      "Epoch 5/50\n",
      "23368/23368 [==============================] - 9s 376us/step - loss: 8.1240e-04 - val_loss: 6.7216e-04\n",
      "Epoch 6/50\n",
      "23368/23368 [==============================] - 9s 388us/step - loss: 7.4395e-04 - val_loss: 6.4011e-04\n",
      "Epoch 7/50\n",
      "23368/23368 [==============================] - 9s 378us/step - loss: 6.9422e-04 - val_loss: 8.4122e-04\n",
      "Epoch 8/50\n",
      "23368/23368 [==============================] - 9s 387us/step - loss: 6.5809e-04 - val_loss: 5.6091e-04\n",
      "Epoch 9/50\n",
      "23368/23368 [==============================] - 9s 378us/step - loss: 6.2747e-04 - val_loss: 6.0820e-04\n",
      "Epoch 10/50\n",
      "23368/23368 [==============================] - 9s 379us/step - loss: 6.0586e-04 - val_loss: 7.3761e-04\n",
      "Epoch 11/50\n",
      "23368/23368 [==============================] - 9s 384us/step - loss: 5.9287e-04 - val_loss: 5.5565e-04\n",
      "Epoch 12/50\n",
      "23368/23368 [==============================] - 9s 382us/step - loss: 5.8230e-04 - val_loss: 5.9686e-04\n",
      "Epoch 13/50\n",
      "23368/23368 [==============================] - 9s 386us/step - loss: 5.7649e-04 - val_loss: 5.3543e-04\n",
      "Epoch 14/50\n",
      "23368/23368 [==============================] - 9s 377us/step - loss: 5.7394e-04 - val_loss: 6.0090e-04\n",
      "Epoch 15/50\n",
      "23368/23368 [==============================] - 9s 376us/step - loss: 5.7102e-04 - val_loss: 6.0753e-04\n",
      "Epoch 16/50\n",
      "23368/23368 [==============================] - 9s 386us/step - loss: 5.6682e-04 - val_loss: 5.1864e-04\n",
      "Epoch 17/50\n",
      "23368/23368 [==============================] - 9s 374us/step - loss: 5.6524e-04 - val_loss: 5.4112e-04\n",
      "Epoch 18/50\n",
      "23368/23368 [==============================] - 9s 381us/step - loss: 5.6364e-04 - val_loss: 7.3449e-04\n",
      "Epoch 19/50\n",
      "23368/23368 [==============================] - 9s 377us/step - loss: 5.6145e-04 - val_loss: 5.1511e-04\n",
      "Epoch 20/50\n",
      "23368/23368 [==============================] - 9s 375us/step - loss: 5.5914e-04 - val_loss: 6.0764e-04\n",
      "Epoch 21/50\n",
      "23368/23368 [==============================] - 9s 388us/step - loss: 5.5970e-04 - val_loss: 5.7228e-04\n",
      "Epoch 22/50\n",
      "23368/23368 [==============================] - 9s 377us/step - loss: 5.5726e-04 - val_loss: 5.1326e-04\n",
      "Epoch 23/50\n",
      "23368/23368 [==============================] - 9s 386us/step - loss: 5.5563e-04 - val_loss: 4.8701e-04\n",
      "Epoch 24/50\n",
      "23368/23368 [==============================] - 9s 376us/step - loss: 5.5398e-04 - val_loss: 6.2111e-04\n",
      "Epoch 25/50\n",
      "23368/23368 [==============================] - 9s 378us/step - loss: 5.5234e-04 - val_loss: 5.7164e-04\n",
      "Epoch 26/50\n",
      "23368/23368 [==============================] - 9s 381us/step - loss: 5.5448e-04 - val_loss: 5.2280e-04\n",
      "Epoch 27/50\n",
      "23368/23368 [==============================] - 9s 376us/step - loss: 5.5153e-04 - val_loss: 5.1358e-04\n",
      "Epoch 28/50\n",
      "23368/23368 [==============================] - 9s 382us/step - loss: 5.4941e-04 - val_loss: 5.6368e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fcf14377a90>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit([train_inputs['encoder_input'], train_inputs['decoder_input']],\n",
    "          train_target,\n",
    "          batch_size=BATCH_SIZE,\n",
    "          epochs=EPOCHS,\n",
    "          validation_data=([valid_inputs['encoder_input'], valid_inputs['decoder_input']], valid_target),\n",
    "          callbacks=[earlystop],\n",
    "          verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement inference model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# build inference encoder model\n",
    "encoder_model = Model(encoder_input, encoder_states)\n",
    "\n",
    "# build inference decoder model\n",
    "decoder_state_input_h = Input(shape=(LATENT_DIM,))\n",
    "decoder_states_input = [decoder_state_input_h]\n",
    "\n",
    "decoder_output, state_h = decoder_GRU(decoder_input, initial_state=decoder_states_input)\n",
    "decoder_states = [state_h]\n",
    "decoder_output = decoder_dense(decoder_output)\n",
    "decoder_model = Model([decoder_input] + decoder_states_input, [decoder_output] + decoder_states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the funtion to make single sequence prediction \n",
    "# based on scoring encoder-decoder\n",
    "def predict_single_sequence(single_input_seq, horizon, n_features):\n",
    "    # apply encoder model to the input_seq to get state\n",
    "    states_value = encoder_model.predict(single_input_seq)\n",
    "    \n",
    "    # get input for decoder's first time step (which is encoder input at time t)\n",
    "    dec_input = np.zeros((1, 1, n_features))\n",
    "    dec_input[0, 0, 0] = single_input_seq[0, -1, :]\n",
    "    \n",
    "    # create final output placeholder\n",
    "    output = list()\n",
    "    # collect predictions\n",
    "    for t in range(horizon):\n",
    "        # predict next value\n",
    "        yhat, h = decoder_model.predict([dec_input] + [states_value])\n",
    "        # store prediction\n",
    "        output.append(yhat[0,0,:])\n",
    "        # update state\n",
    "        states_value = h\n",
    "        # update decoder input to be used as input for next prediction\n",
    "        dec_input[0, 0, 0] = yhat\n",
    "        \n",
    "    return np.array(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.24]\n",
      " [ 0.23]\n",
      " [ 0.25]]\n"
     ]
    }
   ],
   "source": [
    "# example of single sequence prediction\n",
    "print(predict_single_sequence(valid_inputs['encoder_input'][0:1], HORIZON, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the function to make multiple sequence prediction \n",
    "# based on scoring encoder-decoder\n",
    "def predict_multi_sequence(input_seq_multi, horizon, n_features):\n",
    "    # create output placeholder\n",
    "    predictions_all = list()\n",
    "    for seq_index in range(input_seq_multi.shape[0]):       \n",
    "        # Take one sequence for decoding\n",
    "        input_seq = input_seq_multi[seq_index: seq_index + 1]\n",
    "        # Generate prediction for the single sequence\n",
    "        predictions = predict_single_sequence(input_seq, horizon, n_features)\n",
    "        # store all the sequence predictions\n",
    "        predictions_all.append(predictions)\n",
    "        \n",
    "    return np.array(predictions_all)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "test = energy.copy()[test_start_dt:][['load']]\n",
    "test[['load']] = y_scaler.transform(test)\n",
    "test_inputs = TimeSeriesTensor(test, 'load', HORIZON, tensor_structure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1456, 3, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of multiple sequence prediction based on validation data\n",
    "test_predictions_all = predict_multi_sequence(test_inputs['encoder_input'], HORIZON, 1)\n",
    "test_predictions_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1456, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions_all_eval = test_predictions_all.reshape(test_predictions_all.shape[0], test_predictions_all.shape[1])\n",
    "test_predictions_all_eval.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-01 05:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,717.40</td>\n",
       "      <td>2,714.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-01 06:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,985.24</td>\n",
       "      <td>2,970.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-01 07:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,239.50</td>\n",
       "      <td>3,189.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-01 08:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,368.23</td>\n",
       "      <td>3,356.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-01 09:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,499.66</td>\n",
       "      <td>3,436.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-11-01 05:00:00  t+1    2,717.40 2,714.00\n",
       "1 2014-11-01 06:00:00  t+1    2,985.24 2,970.00\n",
       "2 2014-11-01 07:00:00  t+1    3,239.50 3,189.00\n",
       "3 2014-11-01 08:00:00  t+1    3,368.23 3,356.00\n",
       "4 2014-11-01 09:00:00  t+1    3,499.66 3,436.00"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = create_evaluation_df(test_predictions_all_eval, test_inputs, HORIZON, y_scaler)\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h\n",
       "t+1   0.02\n",
       "t+2   0.04\n",
       "t+3   0.07\n",
       "Name: APE, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
    "eval_df.groupby('h')['APE'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0440263391735842"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape(eval_df['prediction'], eval_df['actual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HHnts1uv9/f187WtfY2xsjMnJST71qU+xZcsWanG2FAUhIlghmEiY8LFCwRiOQWMpbkgx\niXgR9dDQTEbO6SAiBAVXCXoEUynT+insETwiBEPE7bffzuHDh9myZQuDg4Pce++98/q7rq4uLr30\nUhKJBFprEokEU1NTTNgtRkEQnCWdNsLvoYfMz+OPm3CTOOcJitEcL6IeGjo+bj7joUP+xk+ckdBQ\nwVWahWA+b+7hYSbV7gGEjUceOXf/r5tvXtjx999/P48//jhf//rXueWWWxb8773hDW9g165dVCoV\n/sN/+A+sXLlywf8PQRDCg9bmJjIx4ecaVKtw4IAIQTDfT63mh8wK0STqHsHxcdi2DXbvhu5uYzt0\ndrZ7VO1DPIKCqzTnCAKMjsLISHvGMx9ECIYQfYp6yad677nnnqNUKvG9732PUpytREGICOWyyTko\nFEyhmGTSvJbLxVsIBkNtqlURglEnKAai5hEslcxmz6FD5vdCwRiO55/f3nG1ExGCgqsEhaC9L42O\ntm8880GEYBML9eItNkNDQyil0FqTyWTYsmULyWQSpRR33XUXn/rUpxqO7+zs5P3vfz+XX345V111\nFa9//evbNHJBEM6WfN4YhtYw6ugwv5dK8RaCwc8eNWEgNKJ1tD2C4+MmR9BWBAZz3ceVajV6xYFq\nNTPPK1bIplXUsdduUAiGvYWEnJIhQwXvBsDk5CQTExNMTk5y44038qMf/aj+WrMIDFIul9kvDYkE\nwWkmJ/3CMM1VQ0UIGqJgKGptxIA0z55N8/xGYb6DjI+bsO/eXv/6bm5KHSeai2BFYb6PH4eXXjIh\n/UK0CQrBZNI8FyEoLIiRkZE5BZzWumVo6NatW/n5z39OuVymUCjw+c9/nrGxMa6//vrFHq4gCIuI\nrfdUKs0WgmGvRLaYRE0Ijo3B00/DkSPtHkn4CHoDIVoe4FrNbPacPAk9Pb5XMM4ewSgKQSvsR0dl\ns2cuDh1yo+fe6bBzrZQpFgPh/1wiBEPGXXfdxec+9zmGh4e57777Gt5r9hZaisUiH/3oR1m5ciXr\n16/nwQcf5Mc//jEjYc5OFQThtNhm8sVioxCs1cJfiWwxac4RdB3beyrsuSTtIMoewakp83kyGWM0\n2utbhKBP80aAi9iNq1LJX9MFn3zeeEv37Wv3SM6eQsE82qqh4G/ohhXJEQwZ7373u3n3u9/d8r25\nGs3fdNNNbN++fTGHJQihJ5MxOXRdXe0eybnDlpIvFs1nC+4FxbXMfKXS6BWKgjCwxm82a3aUe3vb\nO54wEWWP4Pi4+TzZLPT3ixCE2SHvUbi+g59pbAyGh9s3ljBiv58oiP6gEEwkzD07lzPXeF9fe8c2\nF+IRFATBeUoleOYZePHFdo/k3GJzBJs9gva9OBJFQzHoBQl7GNFS02wcRmG+LePjxrPf0WF+l9DQ\nxsJYEI35Dq5ZJ05EazPjXBAlIRjMEUylfK/gq6+2b0ynQ4SgIAjOk8v5gilKpNPGMKrVGkPHIPzh\nJotF1IVg2AsLLDV2fm3hhSjMN5g1q1Awnn3bM9Be39arEEfstdDdbR6jMN/FIkxPm3muVmWzpxk7\n57YvrMsEPYIjI74QDHPYvwhBQRCcxwrAKBgNQaanjfCxPQSDoaHT0+0bVztpFvtRmPOgEJyZibcQ\naMZ6CWzIt+uGomV83DwWi/51LR5Bf6Onp8c8un5912rws5/B974HW7ea38fG2j2qcBFc/1z2Cmrd\nKATXrvU3sMI85yIEBUFwHrv4VqvRqcpmm8mXy74QtDkHEF8hGGWP4LJl5lE8Bj7WMLResyjMN/hC\nMNgqQjyC0fMIFgqmEEoyaaI4du0yc99cFCfOBL8Ll+e7WPQ3qjo74bzzxCMoCIKwJAS9RFHxGBSL\nxki0N8lmj2Bcq4ZaIRiVUMFgw/Q1a8yjCEGfKArBSsVs5DRX/7XXtwjB6AjBqSlzjSeT5mfnTuP1\nl2vcJ7i557JH0G7qJBLGoz087N+nwjzfSy4ElVIJpdSzSqkHvN+HlFI/VUrtVkr9s1JqWeDYP1BK\n7VVK7VRKvT3w+mal1PNKqT1KqS8t9WcQBCFcBA0n1w0HS6FgQsSCQjCYIzgz055xtRtrNNjKmq7P\nd7UKhw/DQw+ZxtNK+SHBgj+/UQoNnZgw4iAYwaCUeAQheqGhtqiXFQRdXSZE9Pjx9o0pbETFI5jJ\nmEelzDwPD/seQRsBEEba4RH8XWBH4Pe7gP+ttb4UeBj4AwCl1OXA+4DLgHcAX1Z+I72vAB/WWl8C\nXKKUunUhA7jgggtQSsnPGf5ccMEFZ3cGCMI5JopC0O4kl0qNpajtKmhvOnHDen+jYiiWy/CLX5iq\ncg88YISO1uE2HJaSSsV8HydOGO+Z6/MNvucgGBa6Zo14BCF6HkEbwm+FYG8vHD0Kzz8fveJmZ0pU\ncgRtP9hEwpy/y5b5kQwzM+Gd7yUVgkqp9cA7gb8OvPwe4H96z/8ncJv3/N3A32mtK1rrg8Be4Dql\n1AgwoLXe5h13f+Bv5sXBgwfRWsvPGf4cPHjwjOZfEBaLKIaG2qqgpZK/qxj0CNqbTtyImscglzOe\n30TCnLvPP+8LH8EYhi+/DN/9rim4EYXcWGvsBsPFNm0Sj2CtZq5n61EB969v2+/VhvbbsMGtW+HI\nkfaOLSwEox9cnu9gaGh3t3k87zzzWqUS3s29pfYIfhH4T0CwnMNqrfUogNb6OLDKe30dELxMjnmv\nrQOOBl4/6r0mCEIMKZcbbx4u30iC2JAi20MQGnME4+oRbBaCrgt/G+IbzCU5fNjMv8u74+eKSgVe\necVshmhtvCmu00oIbtzY6BGMStGrhWCv7c5OPwKiVnP7Grc5oMkkvOEN0N9vfnI5+NGP2ju2sBCV\n0NCgELQbGStX+hs8Ya0cumRCUCn1LmBUa70dUKc4NIbLnyAIZ0oU2wmAv5McFILBqqFxLDFfrRoj\n2u642tdcxhqK1lhIJuGFF4xxFNYd5KWkWvXDo5Uy8+/6nNvz2Hr9lYL16/0m6rVaPKtK2s+cz8O3\nvw3/8i++l9BVgqGh550Ht95qNrESCfjlL2Hv3vaOr93Y0O/g765io3RKJXjxRTh0qDFPMKyVQ1NL\n+G+9BXi3UuqdQA8woJT6JnBcKbVaaz3qhX1azXwMOD/w9+u91+Z6vSV33313/fnNN9/MzTfffPaf\nRBCE0NAcRuWy0RAkKAT7+szzoEcwjkLQegwqFXjwQZNXd+ON7R3T2RL0GFhqNSMG16yB1avbM66w\nUKmYn1TKnP+lkvl+gt+XawRFIMCKFcYLViiYz9fdba57m18UF6wQ3LnThEYfOWKugWrVF8muEby+\n+/rgiitg+3azyZPJwHe+A3/4h41re5yIUjugfN5cw7/4BQwNwVNPwR13+GvVUhcIeuSRR3jkkUdO\ne9ySCUGt9R8CfwiglHor8Amt9YeUUl8A/h3weeC3gB94f/IA8G2l1BcxoZ8XA09prbVSalopdR2w\nDbgd+G9z/btBISgIQvSIqhCcnjafpVxu9AhaCgVjECfaUfKrTVijYedOEy547BhccAG88Y3tHdfZ\nYEN8bThRsWgKSrz4Ilx6KVx2WbzmuJlKxVwDnZ2NHkFXhQGYz3DypD+va9fCJz9pigWVSvCmN5nz\nYGCgveNcauz1bTe5EgnjZXF5TQ+Gfvf3m3P4Xe8ya1gmY8Tu6CiMjLR3nO2i2fPtskcwlzObk9Wq\nOXcLBXj4Yd8juNR5383Or8985jMtjwvD7eXPgH+tlNoN/Cvvd7TWO4DvYCqM/hj4ba3rDuSPAl8D\n9gB7tdYPLvmoBUEIBVEMDa1UjJFQKhnDwd5IgiXmazXfaxgXrKGYzfo76DaX0lWsEEwmjbdg7Voz\n38mkCY0Leo7iiN0MsZVzy2W3c8ZgthDMZODZZ/3Pd+BAeCsMLiZWFFgxYD3ALq/pzUIQTLjghRea\n5829JONGsxB0ea7zeXPdBu/Tzz/fOic4TLRFCGqtH9Vav9t7PqG1/lWt9aVa67drracCx/2p1vpi\nrfVlWuufBl5/Rmv9eq31a7TWv9uOzyAIQjiwHsEoNZwuFs1Nxd5AgmFwQe9Q3ESCNY7LZV8Ium4w\nBz2CfX3wznea3/v6TNGYZ59t39jaje2zZytJKjW7OJSLNAtBW2TCntPZbDz7SDYLwUTCbSFYrZq5\ntcLACkEwoYP2mLgW/gL/PLebnS57BIN9f+21rZRf4CqdDuf9KgweQUEQhDPGCkGbR+eq0RBkvkIw\nrDuMi4U1GmzoDbhfaj8oBHt6TNGQq67yNzYefth9D9iZYs9/e01HpVhMLmeMQuvltEUm7HVeLIbT\nYFxsgtc3uC8ErYfIto7o7fXfW7bMPMZdCFrhFIV2QPm8H8XTLATthlYYN29FCAqC4DTWYLI3WZdv\nJJZCwRiL1jAKCsFgUQHXwyIXiv0+7M3WPndZKAVFgD2Hf/VXfcNofDy81eYWG1tR0M6vFQYuz3et\nZjZwrIdz1Sq/iIQ1HuMqBK0osI+uh4ba9dkWiglu4g0O+mtYFHpjninNQtB1j2DQmx18fWJidpGo\nsCBCUBAEZ6nVfFEQhR1Fy/S0/9ms18Bin2sdTyGodWP4jeuhglYIJhK+EOzvhw0bzPNaLb4eA1so\nJri77rpHsDksdN06P3TMbvhUKn5uWZyw+Z9BY9rl6zvYTD4YFgqwYwds22Ye47rRA7P7wro61+CH\nhgY9gmAKWx05Yj5bGFsCiRAUBMFZ7K55V5efY+Cyt8Bidw2DFUPBfMbgDSZuxWKKxcbvxHWPAfj5\nYUGPIPgVI2s1XyzGjWDfSPBzBF2+xq0QtN6gVat8IRD09i91hcEwUCpF6/oOCkGbugDwj/8I/+N/\nGFFw+DD87GftGV8YsJt6UegLa9u/QOO13NlpmsmXSkvfQmI+iBAUBMFZbH5Yd7dvPLh8I7EEewim\nAk1+zj8/3kKwVDJzHmyn4bLHoFo1n8eGCdpdcTBCUCnjAY2jdwh8j6A1qlyfbzCfaXzcv44TCb+h\ndiLhf9a45f+CmdtSqfH6LpXcDRds5RH8yU/gv/93P28QYP/+9owvDEQpNLRQmNsjqLVpdzQ2Nvff\ntwsRgoIgOIsVgl1d0RKCNuQzKHrAlBy3xoPW8cotqdV8QzHoIXLZY1AuN85x0CMYzCmKa3l520y+\n2SPo6nyDGX8m07rYUdB4DGMI2WJSqzW2CgH3r+9gM/n+flP46c//3H/NruUTE/5mQNywHrSgR9DF\n78JGbmg9WwimUn7RGBGCgiAI5xAbGtrd7S+8rhoNQYIewWYhGPw9TgLB7hzbVgLgvofIFheweaBd\nXf57IgRb5wi6PN/ghwInEiZk7NVX/ffi7O1v7icH7nsE7UZdMmm8fvfc09r7m83G1+tv572z0+3N\n3OawUKXMGp5K+Skd2aw5D8JWCEqEoCAIzhLF0NBKxc8JayUEg7kHcTIe7M0zuFvses6YFXi2UExw\nbnt7/bmPa7GYVh4icLtlSLA4UHe3XygGfAMS4lkIChqvZaXM72EznOeLXZ/HxuBv/mb2Z7Nh/1rD\nyy8v+fDajq0KnEya68F+Hy7ew1tV+R4ZgRtuMHPd0WFe27cvfNe2CEFBEJzFGgidnSbpfnLSzZtI\nENs6wobI2JvK4CAMD/u/ax0vT1FzjzFw3yNoDcVksjE/UGt46CH43/8bnnkmnAUGloLm0NAo9I4M\negSbhWDQIxinsG9o9Phb7PeRzy/9eM4F6bRZtx57bPYG1tBQ4yZfHPME7Zpue6YGq+a6RiYzOz9w\ncBDe9S7z3Irco0cbowDCgAhBQXCUWs0kH1vDIo5Yg3DnTvjbv4Uf/CCcfXoWQrFo5rS5mfyKFY29\npyBeniJrNASNBKWM4eiqx6DZI2j5p38y53M+b6pHbt3anvG1m0pldk4ouDvfMFsIHjvmvxcMF4zT\nJg/4QjAomFwWgrbI07FjZo0Kir5PftJ4ioIewUOH2jPOdmLn3HrLXPYIZrONbY3A3K+vuQbWrPE/\nW6ViNvnChAhBQXCU/fth7144cKDdI2kPWvsG4ZEj/uL7yivtG9O5oFj0+xGBfwMZHjY3lqBBEafQ\n0Lk8guCmoQi+kA+2jti711QVDHqHgmIhTrRqHwFuewSDoaHJZGPxiOCcx2mTB1pf33a+Xby+KxUz\nblsV2K7NwsZxAAAgAElEQVTb73sfvPOdsHZtY3RH0DMcF+w9rlw2m1+//KX5Llz0COZyrT2CShmv\nYLD697PPtmeMc5E6/SGCIISNyUn/xmFvoHHD5oZ1dJgbbhSMRDCfJZ+fnW+wYoXfUsCSy5nvIBGD\nLT0r+ps9guCmoQi+sW89gpkMfPrTjQVkqlUj+Mtlf+c8LjQXi7GPUfEINkdzxNXbD74oCF7fLm/0\nWA9RqdQoDl77WvO4bl2jRzCOmz32HvfSSya1Y2zMeMmvuKK94zoTgjmCQSEIcP31/tqttYSGCoJw\nlpTLsGuX/7uLYRTngmChmFzOL7TgcvEQMLlBwSIZ9qZi8wODIYTVanyajbcKDXXZUIRGIdjdDV/4\ngm8kBL0I+Xz8QgXBzxG0AikKmz32XE0kZnv0g6GhcQv5b5Uj6PJGT3PlZ/tZVq40j+vWNW7sjI4u\n7fjCgJ1zex0kEmadc9GmscK/2SMIxvsb9AieOBEuG0WEoCA4xt695ubS1WVuNi4bRWeD9Qp0dfli\nyPW+U+DnOJZKjTePFSvM47JlvlFRq8WnqIQVgsEy8y4bitAYGvrUU/D4443vB0PH4phD1Kp9BLi9\n5gU9gs3iPlg11LYWiQun2uhxcb5tZcjmys9WCAbzxgBOnozXfMNsL7BS5vty8XsIpnPY83ZgwDz2\n9priQMEc5zD1ExQhKAgOMTZmfpJJs6v0/e/Dd7/rtvA5U6xxYENDwe875fL3MZcBYYWg3WUEvyBB\nHLCGYjAU2nUhaDcwJibggQdmvx8UgkeOLN24wkJz+4gohIbac1Wp2WXkg96ESiU+1za0Dg11+fqe\nmvJbXySLOdi/DyYn6kKwp8dEedjPWCqFSxwsBc1z7vL92+YIAiQmTsLzz7Psp9+pv3/++f6xlUq4\nQoFFCAqCIxSLsGePeb52LWzfbp5PT4cv5nwpaLVL7HrDaa1h4mQVnn2WwjMvkjrxCtTMhxkeNscs\nW+YfX63GI2RQ69ZC0PXQ0GzWGAVPPNH4el8fvO51jR6DOBaTaG4fEYWqoXbdSiRaVzgO5gnGSRjM\nJQRdjfKYnvbO03KJ5KF9cPgwy557jI5/+sf6MevX+8dXKu4XOlsozVEeiYTbHsFSCVSpgDpyECbG\nGfzSZ+Gb3wQaewBXqyIEBUFYIFqbvMBKxXiGjh/3Y8y1dtcQPhvmKh7ishAslyH7zG4SL/ySQgGS\n46NeQqiuewSXL/dvKHHpJVgum89q+wZaXA8VzGZNblAu11jw56674A1v8F/TOn5Gou2j2SpH0GUh\nGIxeGB9vfK+np9ELHCch2Cr0G9wXgiqXJYkZ/EpOwkc+Ut8BuOCCRnEQN69/K49gsejeXEMgNDSf\nI4ExzgaYgT/7M9Ca88/31/NaLVzV3kUICoIDnDhhwog6OuA1r4Ft28zrdmGJW2EB8I3/KDUYLxYh\nu+cYSark6SFFxViD+/a39AjWavEQgtZIDPYYA7dziKpVYzxkMo2FYd71LtNjbPXqRo9g3JrKW+Mw\nWFQhCkIwuG4Fr12l4NJLGzcEmoViVKnVzPdhN/KCuBoumE57c10u1YXgeZww6/knPgGYcEF73ddq\n8csDtoXdgjmCpZK7HsFyGVSpVBeCy5iGHTvgoYdmFQc6eLA942yFCEFBcACbK7J+vblZ2Ipk1jCK\nS+XIIK2EoKu7x5ZCAbITBWooqiTqBkT3kT30fuMvAT9XEIwwikOxGCsEmyutuewRrFQaw6CsALjs\nMvPYLATjVlXQXsOt+ka6KgSrVc9LpMwGQFD0jYzAeec1eoFPnmzPOJeaYM/U5rl1dU2fmYFiQaNK\nJZKYi3wl3oT+zd/A//pfDS0kIH5CsFQyP1YMu+wRNFVDNZR9IdiPVw3sv/5X1q0Lb863CEFBcAB7\nc+zu9r2B4H6O1JkSbDTdnDPmukcwN1Wm4rV4TXkGxDAT8PGPwwMPsGxZY4iJ3RSIMvb8bxaCLguD\nQsGcq1YY2M8yNGQeR0Ya2wmcONGecbYLK5BbtRMolWZ7h12gUjFjtxVDg/mA69ebsO+gOGyVQxhF\ngmt4K6+/i0IwnYbCTAmlK40eQcudd7J2eS62TeUrFfOZrScY3J1r8Dbry2UUNRLU6CVHypt3fvQj\n1hb2N/SNfOWV8KxhIgQFwQGsoZvNwssv+6/HNTQ02EMw6A11PUewUIDsTJUKnQB1A2IF40YFfeAD\nDL6yq8FYjIMQjKJH0IYF2qqY9rPYEODVqxurxk5Ohqv31GLTSgiC+Z4qFTev8WYhGLyOWwnB5qqi\nUcV6BFud3y6KA62Nx7c4aXZoG3IELQcOsPYb98xqKh8WcbDYtJpz6xFsDg92gXQaKJtFK0HNhIVa\ntGbwb/4bfX3+S/l8eDZ6RAhGjLgsInHDCsEXXvBfa+45FSeCPQSDIth6BF28kQBMT2mq2SIlOkig\n6yEmK/CShfJ5Bj/9e6iq/wHjYCy26jHW0+O2R9AKQSsMLNYj2N3d2HuqUolPqCA0egyC2GvcRVFc\nLp9aCA4NNYaPxeHahtaioKfHPLoYGlosmntycaaIIiAEuxtzOJZ95c/oteGDhEscLDatNveU8ltu\nuEYmg+cRNEJwgMbeL+obX2dkpTnR7boWlsqhIgQjRDZrypCHKQlVOHu09nfJXnrJf/11rxOPYCuP\nYPB915g4moVqhRIdJKmgVAIuucSEhnoMTh0ikTZuwLjlCAaNwWD1VBcNh5kZ/9oOev6sEATTdNpS\nrcarYEy12lrsuez1LxT86rfNOYLr1tEQ9g3x8PZD62JQy5ebRxc9gpmMlw+aq6DQKMwHW/n/fNiU\nCvVQusbqY78EdF0cxKU68FweQXCv5kG16tlg5VJ9vgdpiv2emeH8zE7Ar4YsQlA45xw/bk7IuOwo\nxQVbOv/wYf+GuWIFXH6521UTzwb7ebu6Gm8arn8fk/uN5Vek2+QHdnXB2nWsePcN9WMGSaPyWfCM\nizgIwVatQqy3zHoMXPMQZTL22tYkjh+Fxx6jd+fTdBb9neR16/xzOk5GIpi5DjaTt7gsBO2GnRWC\nQaxHMI5C0IqC4JwODvrXd7XamEcYdiYnvU2egiZJFSsHzrt1M/zVXzUcuy6zE7I5EYK4u7Gdz0Mx\nV4VqlQQ1FOY+zUc/2nDchbsexN63w9Q3UoRghLDFBFw1goXWFIvmprJ3r//atdcab5jdcHJt4Txb\ngsVzgp/d5VBZrWHqsFF1RbpMonlXFwDDH36v+bCY3kSJqr9FHqf2EcGQX+s5czF0DIxHsFQClc+R\nnJ6EWpXh0Z3w+79fP2ZkpNFbePhwGwbaJpqFYPDR1dBQu2llq4dakkkz1805gnG4tqG1EOzuNj8u\nioOpKajM5KnV/LDQTlWh/5pL4dZb4UMfqh+7gcNQNEZbnIRgqyiPYISHS9d3NgvlyQxQI+mlcwyu\n6IQ/+qOGnhGbJrZByVz4EhoqnHNmZnwB6OLuuDA3xaIpHW89Px0dcNVVjTfJuIn/uUJDXa6iWixC\n9nim3kMwSQW6TNGYFWs64Vd+BfCEILX6nTSbdU8ELZRWOYIuh46BV0yiCKpYIOEZi8NMwF//NTz+\nOOBXDoV4VhUMCsH+fvPoskfQrlW5XKPgW7PGiEErBK1BnE7HI++/Vb6YFYIubnZOT0NxbBrQfsXQ\noQqq0xMFn/50/diNHPSUsA5VuOBi09xM3uJiC4lsFspTmXp+IMDgppVmAX//++vHbWI/KmtCAcI0\n1yIEI0JzEYG4CYMoUyzCrl1+v6ErrzQ3yJ07YetWU0XUtZj6s8XupqdSjaLP5RzBYhGyJ3OkqPjN\n5D2P4IoVwFvfCkACTQ95KJXQ2hiKMzOn+B87TqViDMRksnFegx7Bctm9JsRWCFIp13eRh/Cqg9xx\nBxSLjIwwq+R4XAi2iAEYGDCPtmqoa/MN/jqdzc4uFANmXfcuecCc1y5uai2UVqLAZY/g9DQUTsyg\nAkJw5dpO/4BNm+oL2EXsQ1GDajVU4YKLTSsvMPhC0KXrO5uFcjoPaDOXwOBl68ybH/94/bgLOUSy\nlIdKmVotPBEeIgQjgg0LtWFELhrCQmvyedN81M7tddfB2Bj8l/9iPAQvvwzbt7d3jEuJLbABc7cT\ncNF4KhYhN14gSYU8vcaAsKGhw8DNN9eP7Sdb30av1aItBIMVYoPz2t9vrgmlGs8JV8jloFg0Daet\nR7AuBHftgs9/flYLiTgVi7EeQXtNd3WZaAiXoyCsmJlLCCrle7rBGMlxyBM8lRB0cU2fnobCRK5R\nCG4a9A9QCjZvBjwvERpKRhyEqdH4YtIqygPc9AjmclDKlBo9gldeaN689lp405sAU/27iyLkTE5o\nOh2Oe7cIwQiQzZoT8dVX4Yc/NFFFLi2awqmZmjKLYiplboyrV8Ojj5oF1N4kw9ScdLGxOZOdnbPP\nc5dDQwsFyE6ZEKESHXWPYDJpqgly/fXmQwP9zEDNuEzsDSWqWIOhs7PRK9Db667HAIxHsJQpY/JK\nmoQgwD33sHp6T0M7gRMn4nOdN4eGdnWZH5e9/nZdmksIgtn0sZ+xVotHC4n55Ai6tKan01BIm52p\nemjo685rPMgTgqs4QQclqJTrLUPiEOFj57y51ZOLOd+5sRnKpSoEWj4Nbr7YP+B3fgcAhbfGF81N\nLSzhoSIEI4D1Br70kjGS9+0Lj8tZOHtsFdhUylRSA3jmmcZckkLB3d55CyVYKKb5humykVgoQDZd\npYKJBbQewXovuZ4eIwah3qNIl0rUatEWgva8bg4D7ulpFAYuGYpgzt3SdB4VMB6CbUIolej/xJ30\n9/vKr1CIhzCA2aGhnZ3mx+Vr3J6jmeMzJEaPwfg4oFm3zj9meNh/Hof2MLWav6kZvIfZMFkXheDM\n8SzFEr5HUClWvn5t40GeEEygWc40lCuxqhx6uhxBl0JDc8/vo0yn7xHs7WNwpNc/4F3vqp/IqxiF\nWgVdrYamYIwIwQhw8qRfgc7mk4yOtndMwrnDCkHrGSqX/VDQYENt10LjzpS5PETgdoPxmRmoZIuU\ng83ku7pMfqDFCw8dtM1qi6XIewStQRBsLt7RYX5cNRRrNTPeUrYxnGhoVWfjgY88wqqC2dXT2vxd\nXNb2Zo9gZ2dEPIITE8y8fJzEiVF44XnYtYv1w/5CFjePoBUEHR2Nc+pyaOjM/hMU6PaFYG9fY44g\nwDXX1J+u5CSUS4AOjThYbOx9vLktiJOhoTsOUKaDukdwcKCe0wyYHfyrrwZgDa8CoIvF0My1CEHH\nyedNiNHYmNkht0JwbKy943KJffvgqafCuQOltZ8jkkya9cR6fu1rYH536UZ5Nth5SqXm9gi6KATH\nXy1BsUCJLtNMHqCzs1EIegVjluGdFDHwCNr5Dl6fPT3mMSgMXAoNrVTMOVrKmg9lw8eG/9274e1v\nbzh2zc6fmTBg7+/iLAQ7O93PESwdPEaJjrr4Tx0/yqot15u8UGDlSv/4alWEoGvzXa1C9sgERbpQ\neNf2QH/DvAJw0UX1CkirOQ5odKUSC49gpWJsm2RythC0oaFhtMfmIrf7KCVMRdgENRgYMOkcQW66\nCYDz8ZJAiyURgsK5wYaFTk+bC8gKQfu6cGrKZVNwJZcL5w23XPbzSRIJIwSfftp/394kazXfcxh1\ngkIwaPwPDrpnNASZPGCbyXeaHoKdnaASDaFivOlN0NHBMky8mK5V0KVSrIWgix7BYtEYO6VirdEj\nuHkjfOUr/gcE1pX317uPV6vxKRjTKkcwCqGh+XSlYc7XcYzEjhfhjW+Ev/1bhocb8webK4JHjWCE\nRxQ8gqUS5I9PU8QU+jJCcIDzmlIESSTq4aFrMcpPl8qxEILBcP/m69hFj2B233GqpOph/mpwsN7u\npo63iXshBwHQnhAMw1yLEHSckycbw0esh2h8XHoJzoexMb/4QhiqNzVTLBob0Ar8ZctMfqAlaDDE\nxQs8l0ewnkuHex7BWg2mD5u+U34PwUDrCEtvL1x3HcutRxBFLZML5bl7rrDzHcwf6vXSL4KGokvC\nIJ0GXdOUSkYQeB+BoesvMaXl7767fuw6jqEKBWzYWByEYK3mhwLb+Y1CaGihANlMjWBRiXV4LoFs\nFj74QZZ/87+TUP7NO+pCMLieR8EjmE5D5eRU3eubQKMGBho39CyeEDwfr0FoqRIaL9FiYsV/IjHb\nTrV9YZ3xCI6NMTXlq9YEmv6Rvvo6VeeGGwC4hL0A6GqFSqkSirkWIegwxaJZdKam/Juj9Qqm0+4s\nnO0kaFSF0ZgumkrDdYGfSMDu3f77wYqCUTcYLHMJQZtbY0NLXNoIKRYhNzpDiio5+o1H0BOCq1c3\nHfzWt7IiUF2ylslFuqBEKyHYyiPo0nqXTpsGxBpdbx3R21Gm64IRc8DHPw59fQBs4DDoCnjegjiE\nhto5D17DzaGhrm32aG3O0bwXxTBLCHos/9G3UDPT9b8JY6TKucR6fpLJxjnt6nLTIzh5JEMxU2xo\nHTG0vq++mdtAoIUEgC6Hx0u0mLSqEtthIivd8whu28YkpiekQpPo7WbZUHL2cStWwBVXcDF7TbsQ\nFJVMkcnJ9qc1iBB0GBv+OTPT6BlKpcyNdHy8PeNyhWy28bsLqxDMZn2P4IEDjeXjg/Mel3DguUJD\n7Y6rrT7nzI0Ez1NwIkeSChn6jEew0wjBkZGmg9/6Vs8jaE6EWq4QynP3XHEqj6CrHqJMBkonZ7wc\nIi8sdAj/w3R3wzveAZickgQaioVYGIngz3nwGnbdI1itmg2qcqnaEBo6/Bu/2tBFfohJkrlM/feo\nh/wH59re2+wGT9AjaNsGhZ2pbXspBPMDe3tZOdJKBVIvGLOJfQDocoVqVXPiRLSrgNvPFtzoWb7c\n38gtlx3a6Hn6adL4CYGJgb7GQjFB3vpWlpOmF2O4VLNFtDat39qJCEGHsYZ/c8NZKxriEEJ0Ntid\n9dWrjZFRqYRv17FQaPQIevUE6gSFYNQNBsupPILgZh8i00w+T4oKWfrrPQShhUfwzW9mOOG7AGuV\nKunj0W08Zec7WFTA9RzBdBqKE1kIeARnVQx973sBWMNxc0zBGA1x6BnayiPoeo5gpWKqxFaq5gMk\nqIFSLPut20zzX4/lTJEo5UCbDx8Xj2BzD0Hw9bFLa/rU9gMU6QHrERwYmF0oxnLJJdDby2rG6KYA\nWlMpVEIhDhYTu5YH17GeHkf7wm7bRgaj/BSaxEBfvc3XLG66iU7KLMMk9dcKRWq19ocCixB0lFLJ\nFIipVBpvFJs2iRCcD1r7QnBkpF68K3SelclJYwzZZvK2bYQljh5BawzMJQRtjoELRoOlWITsVAlF\njRw99R6CSsGqVU0H9/czdMW6el5ZDUV6X3Tjgk8nBF0UBpkMlKZyDT0Eh87vazzone+EVIplTNNF\nCWoVqJTIZuu1YyKLvXabQ0ODwr+52mDYqVTMnJeD1QU7u1g2lIBrr4VLLwVgGdPGA1wu16tGR1n4\nt/L4WyFoH11a09M7j5Gnyw8N7W9RKMaSTMJVVzHIDH3WS5Qv1Td8okqrHoLd3WZdd0oIag3btpHG\nrwyTGOyfXTHUcuONAAxjwvV0uUy1VGn7XIsQdBQb9hncBR8ZgQ0bpIXEfJiaMsZ3T48pwGJ3cMIm\nBIM9BLWeLe6DTeWjnCcWxN48Egn//FfKhJbY5y6GhubSVUpepTnrERwepmVuyYobL8eGhmoSzLwS\n3bKhdr6DoULB0FAXcwQzGbw8okDriIuaqkksXw5vexsK6sWBdK5ArRb9Tb5WlWJdbyhfLEI1bYSg\nwngP6O7yjUavmEQXJeMd8pRupRJt4X8qj6B9dMkjOP3ySYrBHoKn8ggCXHMNnZQZ9CpB14qmJVC7\nxcFi0ipHsKfH/DjVDujIEfTYGDnMJp5Cofp75w4NXbMGXvMa01QeAE11OiMeQeHMsJ6QZm/gihV+\nGKEIwbmxhpQNu7MXbtjK8Nv5TaVme/yUavQIRj2EyDKXh8gWFkgk/NLzrjAzA6VMyes95TeTn5Uf\n6DF867UNHsHcyZw7VdYWyKk8gq5WDc1MVyhlqwSrRw5f1uz6BW67DYAV3g4yhXwsKofOJ0fQNY9g\nJgPkjBCsV4rt6vLDyDwhCDBAGkrGI1irzU7/iBKn8gja0NBEwrwf+jUul2P61ZyXI2g9gi16CAbx\nCsaswNv1LZYjXznUXrvN4t+50NBt26iQooA5YVVnB4lEYu7QUIC3vpU1mAVco6iIEBTOFOsJCe4a\nXXSREYLWgyDFYlpTrfqiqlkIZjLhCsMJtgVp3iG85ppGj2DYROxi0cpD1Ndnvoegx8ClnLGJkzXI\n5cjTTco2k+/qnJ0f6JG66c10Yu6mNRLUcnlmXo5eOclq1RjCtpKc5ehRuOsu+OpXfQHoTHEBIHvo\nJEU6fGOxs5Oh81tsI7/nPQCch1mwdLlMtVCKfOXQVkLQ5gi6WjU0lwOyWUp01sU/XQGPoBc2BiY8\nlHIJ0PXw0Khi57g5TBDMXAe9/qEXB88/zzQDvkewqwNSqblDQ6EuBM/zvES6FJ5G44tFK4+gk6Gh\n27aRp5siJr870W0eTykEb7qJ9V5TeY2ims61fa5FCDpKPm9EizX+UykTFppI+B5Bm18mNHLihPle\nli3zPQsdHWYhqlbDswCVy2aO7cJ46FDj+//6Xzd6BOMgBLWOZqjgxKEZ0DWvh2DVXMTJ1JxCUC0b\nrBtLYG4o6Ye2Lc1gl5BWFWJrNfiLv4CtW+HnP/f7aro039lDJ+thwAlq0NdnqoY2s3YtXH89q7Eu\nQEVtMh15IdgqXLDZI+iaEMxmoZbNUSFZF4Kqu8tvPL1pU71EcL0qcLkca4+gfe5KuGD16V+Spd+L\n7IBknzEwTukRvPxy6O5mDaY6jNY1qoVS28XBYmLnurklkHNCcOdOsvRRoRPQJHvmJwQvxBpzimq2\nyInRalsjmEQIOojWRgi++qrv/duwAe69F37zN+F73/P7sEh46GxsWFVz2F3YCsbY1hHJpPHuBheK\nwUFTXyDoEQzLuBeTYM+p4I3Ca7nWYCi64hGsVGDmyBSKGnn6vPxAYwnNJQQBegY66s9rJEg//txi\nD3XJCQpBO5/ptJ8zlUj4RZ+cEoJHJinS6XsE5xKCAO99b73XnAaqU+lYhIbWav71rpTZrAt6/F0L\nDc3noZwxg7ZCcGBZ0t/MU6oeHlrPCS2ZfLEoh/3bOT6VEHRFHBSfeZEcPRS9TZ7koBGCp/QIplJw\n5ZWmXygAiupMnmPH3NvsmC+thKD1CDp1/z5wgCx9lLy830SXuSefUghecAHnrUrQTcGs5yh0eqat\nVWJFCDqI7aczNuYvkNUq/PSn5nm5DHv2mOdRLkF8JhQKZnc1kZi9OIdVCKZSJiw0WDRk82bj0UwG\n+pYWCg7kUJwlc7WOsELQJaPBUixC1msmb3oIVqHL7CzOlSMI0LPCuEE1yngEn9o198GOEiwMZIVe\nqeTPcbD5sCtGk9aQG01TsuFEnkdweHiOP7jtNjZ4oURmB7nA6GFHPuwZUqmYHzvPVgAGhaAr823J\nZk37iGCl2GXndTQe5AnBlTYn1CscEmePYHBzL+xreu7F/cwwgPYyuBMDffT0+BErc7J5M+dxkh7y\nRhxk82gNBw8u9oiXHrvJk0zOLQSVcqCpvNZ1IWgqAWsSnfMQgsDy6y6hl5wJDSUFU1Nt9QCLEHSQ\nfN6cg8HiIcEech0dxoPUqspk3LHeg5UrZ1djDKMQtD0Em4XgG99ojCRbKRPM4hr1yqFzNZNv1WDc\niR1FvHkey5KcTw/BAH0jfsnqGgnShybdcovNg1bNpq2hAI1hwK40nC6XoXBihhKdftXQvv65PYKX\nXsqFF5gPrFHUSHD8hWj3irHFnoJCEPzrWynfoHSFXA7KeXNCWyE4uLpJIdTLy3s39HKZWk1HWggu\nxCOYDXO7VK2Z3jNKoV4xtEair/fUYaGWa65hGdMMkDbiIGd2OfbvX9whtwM7zx0djfdoKwTB3+AL\n9cb2yZOQydSFoEKR6DQ786cTgkM3vd5rKq+okISpqbZWiRUh6CD5vBF+9iLp64MDB/z3UylzsaXT\nEhraTLB3YDPBgjFhMDDyeWM8WNEf9P698Y3mccUK3yiuVqPfVP50HkEXhWChANnxPEmqZOmt9xCE\nUwvB3sFOSCTBegRrfbBz59IMeoloVTTEzq99nkz63iEXdHApXSCbrmA1a4Ia3cO9DcZvMxveczXK\nEw8VEswcnQq9d+RsqFZnewSDjy61E7Dkp0uUygQ8goplIz2NB115pakyyUlAg65RK5QiGxoazPkO\nhvrOlSMY6jX9xAlOpDu8CpKapKpBd/f8hODmzSxnigEygKKaN1/Gvn2LOeD2EBSCwfXa5gjCwvtG\nVqttaLHiGd15eijTCakkiYQ5UU8nBFfcem3AI5hET6U5drh9i5kIQQfJ542HqMOLKtmwAXbv9t/v\n6DAL58SECMEghYIRVqkULXffUymzENVq4QhBscV+JiZAFXIk9u+F8XHWr/cFwvCwf5PUOvqVYucj\nBF0rFlMsQm6iSIUUNRJ1j+DAgH9jbMXAAJBKoTEewRkG4Llo5Qm2Chtr9vop5c91GK7b0zHzy731\nYhIJaqjuboZXtWgWGaD/1/8NvRgruEoSxicYPeTICX4GVCqNIcCthGC16laLmNwrk5S8ZvKKGnR1\nmmbyQVIpeNObWMF4vT1MNVuIbKSH3XANbuZY/umf4NFHG9f0UAvBnTs5ycq6RzDVmQDUqfMDLVdc\nwfJklgFmTGhopQblUqQ9gp2djfdo2z4CFhYamk7Dtm3w9NNLbO96QnCKZVRJoJLJerHGU923Abpf\n/xqWdZoPX0NR05pjv2xfBbBT332EUJLPm9LCwVDBoAs9KASbe8/FGRtes2xZo1fhmWfMAnLTTWYn\nJ5834aH9/a3/P0uF9e5NH02TfPVV4BU4epRrblgHXAIYQRusHBr1aoILCQ11SQhmpyt+gQHPI3iq\n/Bo9ewoAACAASURBVEDwzs+ODiiZkMFplsHzzy/+gJeQVo3Fm4VgsLVEqA1Fj/RzB7yKofMoFOPR\n8+ar6U3uIlvtMzvItSqjP9zGxstuPPUfOkpzjuDx4/C7v2vW52rVvF6tmmv8tPlXISH/6rQXQub3\nCa23jghyww2c97++jykNpKjlC0xOtjrQfex1nUw2rtdf+IKfH3fttY54BHfuZJwV9dYRqW5joM1L\nCHZ2svzytQy8MFP3EjGTYd++YbRutFdcp1UBMJgdGloqnTo0VGs4fNicJ/aecPQorGrRjnVR8FT6\nBF5yd8oIwYGBecyXUqxa2wEHqc/3nmezVCqzU5aWAvEIOkguZwSe9Qg25w+kUr4QnJhwK3xmMbG7\nqsG8uqNHzc7j1q3w4IPhaixvhWDu4AnjJfK4aOv/V3++fLlvLGltwtajzEI8gqE2GgJkMqaZvJ9b\nYoTgqcJCwQs/SaXAyxuLi0eweT0LFpJxwiP44iGvYug8CsV4qGSC5ecZd5g1HI7/c7TmOoj19iUS\nxmv0gx+YPY4nnjC7/04IgybyYzMmhAxOKwRX4bs2dL4U2RxBey2nUv41nM3C3r3+MU8/7ciavmsX\n46ysN5NPea0E5hUaCiy/9jX0kSNJzWz2pGdIp6OX7hEUd3ZdTySMh7A5R3Au27VYNLe6AweM3bN+\nvTmHghWlFx3PIzhphaDnEWx5Tbdg/Wv7SVIBbz2fOp5n69bFGerpECHoGDZfrFo1J35fH7z8cuMx\niYQRidWqEQZRW0jOFHszDQrBYG7lrl311KxQFIyx481Ol4048Fiz/Sd119/QUGPuYNSFYNBwOF2O\noCtVBScngVyOAj2kqJqQsM7TC8Fly4CU2Q3SKNIMmrujCxVT5sl8haA1EJ0Qgrtf8SqGmoIS8/EI\nAgxv9BNPqiQZfTa6jcasR1ApY9wFr+XRUTeFYGEi6xUIsh7B7tZG4/XXc15i0g8NrdaYGiuGIm/9\nXNPKI1gozO6Pa6/5ds63zVudk507mWTI9wj2L0wIpq69ml6y9JMx4aEz5gYXtTzBVnnfNg/UCkEb\nGtoq9Hty0mwGTU0Z8XjllXDxxX66zJJVyvc8glOYi1h1dKDU6fMDLUOvX88aXkUDFVIwPc2Pf9Se\ne7cIwXmSz5uG3u1ejEslcwEkk2axHBqCF1+cfZwtsz0xIZVDwSwq+bz53oIhn8FKTeWyWUSU8pr/\ntnGuy2UjRhNKk8kmGjyCa2tH4O/+DpjtEYy66A+2E7BGQfAG4mKO4MSxHFTK5OgxO4RKQWfHPD2C\nyXolyZ1cxg/G30LtWHR6xrQSgs3GmGuhoTP7xrwcQU1inqGhAMOvGcYWBqqR4PhkZyT7CtRq5sd6\nBIMhotBYHdaVaxwgP5Gf5RFsaTT29dH9xivowhQM0UBtajoUUSrnmmBfWHsNB6vFgnluN/3aOd+/\n/KXxTs61z1ba8TJpBih43v5Un0l4m68QZPNm+sl4eYKKasYsZlHLE5xLCILZ4O3oMHNu+2U38/LL\n5v+xYoUpmjc8bF7budPYTKOjSxQFV88R9DwLgdDQ+TB4yQjnp8b8UOBKhX95ONuWOg8iBOeB1vDS\nS2be292Xz+av2TjiYrH14mjDQ8fH2z/mMGDDQpvzA5tL9u7ebXJOtG5DFaoAtnUEuSz5WkddCCq0\nCRv65jeB2R7BuBSLCQqDnh7fcAhWmHPBSDTN5KcDzeRtxVB12hxBkwuhINWBRlGgmy/xf/Pbd1TY\nFZGWgq0qCjY3ElfKb6kTeiE4M0PmRI6iJwiS1KC3d15CcPWalHexG8NhO1ex78G9p/0717Bzbg3u\nZmFgvQXgwHx7aA3FqYLfeJoadM8RGgpwww1047u3a1PpKGr+lu1hoPEenUj4ETrtmu9y2dgDhcIc\nY8hkKBwdI0O/l/8Lqf55NJMP8vrXM+AJQYBqsQzVSmQ9gnO1Cwnez5ttsGAhv8svNw6Pp56Cb30L\nnn0WHn/crA2LXjSmUjEJikDaegRTqQWFhi4bSjC4qotB0kYIArXJNA8+uCgjPiUiBOfBiRP+Cdlu\nj0s+b0IlbH5gcyjgRReZx2D+YDv7k4SFYKEYSyYzu+/e3r3+otTO8FDbTL44agZuQ0NXM2rEwjPP\nwM6dDR5BiKSDoAF7EwmGitmw0GLRfBcueQSLRcgeT3vN5L2cgc7Tt44AIwRHRoCODmqBpXz3bs1v\n/zZ86UtuhEqeilZCsDlcyBYOKZcdmPMdO+rGorINiBPJ0+YIgsmDMaLfhIZOsZyPfnYVjz222INe\nWuxuvo3IsCGilqAH2JXw70oFSjMFyl59vlPmCALccAN9GDeYRqGnZyK5ttu5PpUHJ5Hw7a92Xd/B\nf7dlL8Pdu5lgmBKdlEmRSCVJdiTqUVvzoqeHwZWddY9ghRRkc5HzCLaa82CVzaAQbBbddsOvp8fs\niT35JPz4x/77tZrxDi66zXv0aP3mNM1yUAlUIrEgj+Dy5aBWr+YCDteFINPT/PjHS5/dIULwNGjd\nmEc2NdXeFBwrBK1H8FhTmsg732lc5rZyaLUKL7yw9OMMG60KxbRaLEol36vWbiGYy0FxzMQDWY/g\nGgLu3W99q6FqqNbxEYJBMdDbayJl3/EO+PjH/cqpLhiJppl8hgQVMtYj2D0/IdjVZfIjLjt/hg4C\nX0gmg9amwMZnP7uIg18CWgn/5nm153/dix5mXnyRNIO+EOw1nsH5GItr1sDIUBFTHMgYDsWpAp/+\nNHzjG9FJDW0OHWsVKhjMJ3OBSsUUhJpXaCjAW95CP747pJbNMfVK2E/uhdOqKnAziYRfvK1da3rw\nPGu5xuzcyQlW1UO+U13m+hwebjx3T8eqjX30e0KwRCdkMxw6FPLG6gvkdB7BYFRPs0fQivC+Pnjs\nMfjnf258v6/P2LuTk4tsv3mioIZihn5IJlHKzPV8cwSXLwdWrWIdx9A2I3h6mldeWfqabyIET8Px\n40Z89fQYg7NabW9FyaBHUGu/xLLlyivh6qt9IQiNPQbjSLlsFpDm3ZqgEAyW7D1yxDw2LyRaL92N\nKJ83Y86Pm5XPegTXEVD+3/oWywdrDTea6enoGIStsDeR4I05mYSvfc187mzWX0RdEYLZk3k0CSqk\n6h7Brq7T31C6u805vfE1HfxH/l9u5hHzRsbfst661QFxdAqssRD0CM4lBPP51sKgUCA8DblfeokM\nA17VUE2yx1hA8/EIDg7ClZdXuJCDVO2tO2sspfvvhz/+Y7fn2mKvcesRLJcbPYLBvpGuCMFSukCx\nUKPiCfgENVRX59zeg1WrGOizv5ic0Klt0QsDbuUdatUexgqCdoWGBv/duYRgQw9Br3XEvPMDPUYu\nG6aTCj0UzKZBJku1Wo9CjAQLCQ1t/q6zWXN+PPccPPyw//oFF5jaD1aM7dq1yClRgWbyOXpQyUT9\n355vaOjwMLByJUk0F3DIvFjIQ6nY4OVcCmIvBE/VdL1W84XWxo3+rm07jYps1s8RnJqaHSK3aRNs\n3mzetxfToUNuNd4911gv2eBg4+5cUAhed53/3BYFyuXM9/3qq7BjB/ziFyYUYffuxRdb1vOcmy6R\npEYC8w82eAQPH6bnmScaFtFCwR3j6Exo5SEK9htKJMw1vZSi/WywzeRtD0HbTH5k5PS9iOy866Fh\nuinyaT7Ln/OfGMiNQs23rFwtFqW1byDauWyVBxgMBW5lKO7caQyHMFQCLu/YS5Yer3okJLyCEvPx\nCA4MQGJ4OVfwIu/gQbM5lMmCtzY88QTcc8/ijX2paCUEmz2Cdp5duMYBcgdGvR6Cngjs7KJ/IHFK\nb9HgahMvp4EaCSafPTD3wY7SShQ0F2lTyt/gDBYKWkpOGxq6axcnAq0jOnoW0EMwwMjmNQAMkqZE\nB9pTwFHKE5wrzz/4/FRC8JlnYPt2/7VNm+Df/lvTBxrMOvnCC2ZDf9GKxnjxuhn6jPhPeRs8CwgN\nHRoCUimq563mQg5RP62np3n00aWtURFrIVirmYqbO3aYC615gTl2zCw8/f3mgra7tu3MEzxxwoyz\no8OrKhmYwSuuML9fdZVZPG2eYNwLxrQKC9W6UQhefbU/v5WKLya2bTPCb2zMX7hefRX27FncG9LE\nBFAskismvF4zgFKsveGihuPUt77ZsANVrUY7PLSVEAwaDjZfrFIxArHdVX5Ph2kmX6bQ1Ez+dGGh\nEBCCHV2Uho0B8Uae4VJ2Qda/g7oqBG0BiWTSN/yb88XA/G69RM2bIMGiT2ForVLcfZAMA/WbfrKv\nh66uRkNoLgYHgd5eap29vJZd3Mfvs7w2Dnn/Qz/5pDsFVObCrrNz5QgGPYKufNbs/uNeWOhpmskH\nWLbBv2FpFFM7opfsbw31YOhjq/YwtZq/nrdD/OfzJvdszx6znsy69+/cyUSwmby3wbNQITj0xovp\npMgAaWokqGQLgI5UnuB8PIL2em8WggcPGpvd2raveQ188IOmaMw11xgbz0bDPf+8nyZyzglUDC3T\nieowFUMX0j6iv984bKrnjbCKUVbi3aCm05RK8NBDizT2FsRaCOZy/s3myJFGT0+gKBAbN5oJXr7c\nPM7MtCdmu1Qy3khbEKPZ4H/DG8zjmjWmiESnSUegVqNtjSrDQKv+gcHGo52dJoTjda/z3z9xwjym\nUmYxv+QSuP568x0nEkYMLqZncGLC/CdHn986orePNR/61cYD/+EfGBqs1BfOWi1EYXCLQKvQ0OZq\nc0r5XsKwe8IzGSjOlCnQg2JhQtCKB62htG5j/fURjtdDBsHdTaBWjYer1dlC8FShoaWSb1ierqJu\nuWw2BBfN0CwWKR46zgwDKE8KJvq65xUWCl7IkVJUh1aSpY8reYG/4k4Ga/6NQOtwCN6zITjX9ve5\nPILNFWTDSu7QyflXDPVYfskq75kXGnq0lQJxm1Y5gq28OLYyMMzhkVtkdu40Hvdf/MKIwYY1olyG\nvXuZYMjzCEKqz2zsLTQ0tOvSCxlKZhjw8kPLFXMzi5JH0M5v8NqdT2hoMES2owMuvBDe/34/rSeZ\nhFtuMc8HBkyl/0X73jxlPsEwZTpJeK0jYP5C0PYB16tG0Cg/tWParOc/+tE5HvMpiLUQtEKgr8+c\nRMePm5OnVjNFgcplc/NdscIcl0yaSW5XUY7m/MDmnf4rV4/CX/0VPPkkV1+l67smAP/yL0s71rBQ\nqZh5Vmru/MC1a837l1/uvzY5aXaY3vIWIxDXrjUL1NCQycO058tiicHJSdDj42TpNQVEAAb6Wfsb\nb2ncZpyeZmj6UP3XKBeMsR6ioCFoX7cEvUMQ/pwp20w+TzdJKiZlvKtzYR5BDcXVG+qvj3Cc0nS+\nbqy46hFsZSTa+Q1id2JbeQRzObOLvH272Vw5lcg7eNBsCNoc4XPO/v3kdacnBIFkimRHct5VBa2B\nURtaQQaTQLaKEwyXj/PKKyaCpVz2N7FcZSGhoa6EwWePTFCig4V4BIc2DYG3ZVAjwVShy/3JbSIo\n9i2tNtkTCX8tX2ohqLUvQLq6jO3QMIZ9+9CVClMsJ0cvqEQ9R3ChHsGO7iRDqzvp84SgKRiTjYxH\n0Ib7K7Xw0NBczqzh1uP32tf6IjCfN3N0xRWmmnZvr1k/nnxykWp6eB7B44xQIoXqSNXbeM1XCNaP\nXbWKKkmu4ylztWcyUKuyd6+pYr8UxFoI2pyR1auNpyeVMrupzz3nGwObNjX+TTvDQ4MVQ21PG4Om\n6+RRLv0/XgP/8T/Cm9/M1YUnGwrGPP/80o83DNiw0MHBxn57zUIQjBfVGmWlkt9cHswCtn276Vez\ne7cRh1YM7tp1bsVgpWLOzfLJNDWS9dDQ/hVdDAyl4Dd/s+H44YPP1sdZrUbXI2gNhFSq8QbhcoPx\nibEKFPLk6fU9v16O4Ono7Az0TFy1vv56hRSvTPbwyivme3LdI9gs9JsJ5gg2C4ODB+GRR8y1u23b\n3Ou21r6NvWiG5p49pBmsh48lOkylufkKwb4+77MODZGnlyoJXuR1HDqWpFg0a1YmEz2PYKvQ0GKx\nfWGCZ0LulalZFUNPJwSHVybqfSNrJDjARnLb9yz+YJeQVmGCpxOCS725VyoZOyKZNGvuxETTGHbt\nokAXGfrJ0o/q7KwLlAsvXNi/1d0NQxsG6KKIomaEYMY0GY/CBq+d22Syca2eyyMYvH9ns+Y7sJFu\nqzyH+d69Jkfwt34L7rjDRG8pZUIvd+9eBDGVy8HoKBo4xlovNDRVH9eChWBPD7XlKznK+VzLNnMz\n8tTrUhWNibUQtB7B/n4zIVdfbXZ8pqfNTWh4eHYFoHYWjAl6BEdHvd2QSgV27ODyF79DKu9XQ7j6\np59vEIKHDoXfO7IYtOofCK2FYLNXcMcO81gowHe/C9//vskT+MlPTGn+8883C9ro6OzqrWeDLYNf\nGDcnaL11xEZv2+xDH2o4fsW+p+rFQWq1aNwwWmENw1Sq0Vhv1VfOhRyiahWmD0+j0F4IcBU6OkEl\n5uUR7OwMtE4YNidxmRQ7uAxdLAOafN59IRic31ZCsC6GWwjBl182j6mUuUbnKgw2NeWHKi3aOrln\nD1Ms9yuGdhprcb5CMJHwds6HhqiS4Ce8g+/y6/Rkx6lW/dxY151G8wkNtYVDnPEIvpr2QkPn7xFc\nvx6SHaZMmCbBDAP8/bcj1EeA1h7BVuH8iYS/5i+1HZPPG5swlTK2VzrddI/1KoYW6TSRHZ3JevrO\nBRcs7N9KpWD1a5ejUCxnmjId9TD/YBszVwlu5gav3a4us6F+9Ghj+4jgMTMz5nu3kW6rV5t5+c//\n2bfH9++HP/9z8/v/z96Zx9lVl/f//T13m7vMmpnJMtkJECAEwr4oIgiuFLXS2urPomKtWopbtdVq\nkWqttFqt1KVVqNRa6oYKlCqioCIkQIAQkkxC1pnMvs/dl3N+fzzne865d86smQncic/rFUju3Dtz\n537P+X6fz/P5PJ8nHpfr6+c/n2evAI9jaC/LwAiglCIcljM5Epn5t9q4Uf5falvNDjazkT3ygM1g\nPPjg8fE5OGGBoNdEIJGQ/8fjAgZjMUnwK9lAEHlhMDi5VflChpcR7O2FUHYMnngc+vo4k/Jhgc3P\nPMia1WbZDXUizhOciVFMW5v7dy8Q3LNHmOGvf10kw944fBi+8x3XxWw+GeJcDpIjefJJyUz16IgV\nm2w6+txz3R0EaLF6xHaYxd0j6D1EvECwsk/ICwRfzMWPXE4SxIAzQ7DonCIzAYJeZ+BCoolSNMFv\neIk8YAkyKBaFta7G1iI/aajf76GloZ5CqhMaBNfVSYK5a5f/wdrbK/0kP/mJqCcWxG1u71571piY\nhgRr3FljM41EAmhsxCTAE5wHQDg9TCFvUijI+16MQLByoDzgsKDVEOn+5MxnCNpRVwdrmmWjM+05\nY//z8NJpe12rKfyKPdMBwZkU91IpuZ/n4z5OJuVPMOiyUZ2dnifs3k0frSTt3t+g7Ri6ahVl7Tkz\njVXnyeZfyzgFwlj2OKDF0CfoPcO96/j1r8N73gNve5u0MXlVHnrP7+6W14fDkqtHo/DpT08s7uXz\n4omxdavsGzqPm7ewgWCaGL20omypWTg8OzYQhL3csAFKy0XR081yImSdxHV8vOJaW6A4YYFgJiOb\nRCTi3twg1YjzzxdjEA0QvaFNY+D4J9zj47IhhULQu7OP4LNPORnvWVRMoEyluGBVj3NDlUoijTqR\nolSSz0ypckZweNjdhKLRcpC4YoX73GxW5tN513n9eneTyufh4YfhgQfmN/nK5SB9ZIg0opcIUoSa\nKMvX2qUmpeCtb3We30I/yr4OTgRpqLdHyNsPqENLx+DFzQjmcpDsk2pUjohIgCNhAgG3L3mqUMrd\nuyxl0LfxMh7hUmrIEqCElctTKgkYPp5W1PMVftJQPxA31RxN3R8Zjcq+efCgWxzyfs+jR8UIYmRE\nEogFYdXb2xmkyRkmH4xKljgbIBiPA8EgZoN7gcRIQ0E+rMXACBaLsoZekOBdY28fcLUAwexg2h4Z\nohnBmmkZwWgU1q8xCZPHtFO13Fie228/Dm/4OIVe46nmhIIrDdVjnaYK7QY/X/2+2nlSM4Ig+4UT\ne/bQT4tjAqXv65PKDb5nHE3nbyBBkgRJLKCQyoO1OJxDvaoe7yzQhx+Wv1uWqK3AbfHQ14j+zEMh\nkYXeeSc88YT/z4lGpdj/6KOy3//mN/P4S9hAcJwEgzSjQnJvzgUIhkIyuixxsvSC5O1ikTUyhh4L\ndDz6BE9YIFjJBnrDm2Dp6O+Xiy6dfuH6BHXlI7/3AOmeUTlQEMbodHZNeP45POkmitaJ5xyqk8JE\nYur+wMr+Ey8rqCMSgeuuk4rVDTe4TeC6T/DHP56/ZDuTgXTXMFlEChqgJEYxKzxPeu1rnb82M4gq\nyklqWdO7I1Zr+DFE0ejEni4vUHyxA8FUf8bZ/DUj2NpanvhOFd77+77EH1JEqtEBTPKZklNdr0bD\nGL8kcTJHQf15pVLu872gKBgUpceRIxOB0tCQHLaBgBQCTXNhBjhb7XsZosmRhgZjsngzlYaCa3hV\nahHW4Ax2cjGPOh/WYmEESyXKiph+jGDVzEzN58mM5UXmh2YEw9MmjdEoBJvq2MDzDhAkneb++xeH\nTBD8HST91lTLgTOZ6YHg0aPuvt/dfexqCL13BoOyRxiG3GP5PPLN9+xhgGYXCNr3tZ+ibCYRX9NM\nY01WCjxA3gpCNrOoGMFK1Y73vOvpcYs/uZxcI4WC5L9KyToMDwsQ9MaaNeWyzERCvvfWrSIPnTen\nfxuRd7BK9vJggFBI3u9sgaCWk175B82oqFv4HzPjTmKj2xsWMn4HBH2AYGVks3D77XDvvdIrpg9u\nPfT7eESh4I6OGHyujxCufuIU9lJz05/BJz5R9poze37uVLC8zlcnSvjJQsG/P9AblUCwrU08ePR4\niRUr4N3vhksucec16jlD8xHiGDpMmpg7TD5Ry/Llnidt3Ojsng2MYJTyYElh4IWcc7mQ4QcM4vGJ\nckANBC3rxZ0o5nKQHMo7MwQ1EJyJLFSHPvhMEzoTIhfOUkOEHFahSKkkX6vGPkE/2VglENQAQRd6\n0mmXUejtldcGAvK6aFSuhz17yr9HX5/cu/G4C6znslda1hTnwego+b5hxqgTcwHUnIDgKafYf2lp\n5Rp+whqOsJQeKMqHVO2MoGXJmnlZwMnWvGqkoV1dpIm6jGAwAMqYlhGMxYD6elbSRQM2RZ3LYpVK\nfP3rC/6uFzy8LtB6HfV+VRleF8mpgGA+7/brB4NyjRzreagL8NoAJhSS75lKIahzfJxBljBOQu5r\nmxGcKxBM1Coa2mJEETSrDWMOHVrAAenHKbyOwHqvtKyJhZ7RUXlMzwNOpSQvCoXk7z/8Yfle26BG\n+afz7uLrt/Q6Bj3xuKyZacK2bfNomGhXYY7S5oyO0Hn2TIfJ69DnzZIWg8vOkkSmiSGGaSA3KJ4f\nvwOCCxgaCM5k4fbudStMBw5I4llTI4fV8ZJcefsDB7ryhHDLG2e+eRN88Ytw+eVlr1ny1M/LLObH\nx1/cPVPzHX7zA2F6ILhypfSK1tTI+Ih3vGNishYMwtVXw9lnu3KR+dKh9/UBg4OkibpOkrWJsl5G\namoc7Ukjw1JlLhZPCEbQm/zFYq77rw49gLhQeHFf79kspIdz5LwS4FkCQS8jqF+4ig7qGYVC0bHr\nrmYg6F3vyv4hr9ETlPdud3RIQvHLX8J//Zf0+RqGAEG9n5dKwgYODEjioO9l7x4x09i+XeT3vmBw\n3z5ytrNggRAEgwRtSdFsgODpp8Mb3gDveHOaZfSSJsYKugkW5ZfW9/8LMed2PkK/b29y6DdgXEtD\nq8I1tLOTDFGXEbRNgmYiDSUQQMXjXIhn/lM6zdatcr1Vc/jJBCtlwDr0Y8nk1CqPAwfk++oC/3zs\nfRoI6r1BA8FkEhkwCAzRKIxgKEgwKBfuXKWh0SgsWVdHAJMastJbmkqSzx+ffrGFjJk4QSslYE+v\n+fi4CwQDAdnPywzEenv45MNX0nzjH7Hm8nV87eTP85pXljAMdzTc4KB4OsxLHDiABXSxggKhMsfQ\n6e7pytDXVKEAl11TTxtHaWIIC0V/v+Qx+/YtPOF0wgJBnTzOhBGsrCA/+eTxdw91HEOtPMPjAYJe\nRvAP7Eny551Xdlep3btoaXCfVyy6evfFHqbprrH35jTNyY1idCgF114LH/0oXHWVyyh8/evSmvfO\nd8Jf/RV8/vNyLRw9KgnJXJJHvxjoLVIaHnP7xgCjLjFxJpFNUTYwQsAGgmAzilVoDjJdTMYI+gFB\neGEMnWYTIyNQGhojS43L/EZjxwQEQ+Q5hX00MgRmCcs0KZUWjzS0Egjqqrvfmnd2in14LifJ5rPP\nSmHgyBG3WDI4KM+JRNyKv2XNPnlMpdxCm2+iuncvOSKMU0ueCIRCzs+bTY9gKCT7mdp4KodYC8Cp\n7CVRHAEsB/hX6wiJyvXVv483vD2CxeLxcdU7pujsJEvUdQ2NSPY3nYwsErGv64YGltPNZmxKw65u\nfe1r1b3Pe2WCGtBPBQSVmuL+QvKjnh65Lh58EH76U3jkEbnH51owME13r9Cqg3BYrsmuLpzksJOV\nlOx5cnpQ+GxnCOpQClZukgSzjlFnliBQ9X2CfkDQ7xoeHy+X+4+Oyvru3y/r4bRuDQ/zrvYPs8V8\nUv6dyRD5+If58H9tYcvSLmpr3XW7997ZETfPPScsYtn7syw4eJAcEQZopkgAFQrNaXQEuL/H+DgM\nnfky3sgPaWGAICUK6SLj4/K7L/R+fkICwVxONpxgsHx+iV8UChOp2R07XCbxeMnwHEZwoJshGl1p\naCzGSVtspFNX5+oX7Vga6Hew4YkEBDMZ2cRjMTfBA9lEdGKZSEzNCHsrVXfcAXfdJaDvwAHRnd97\nL/zsZ9I7qmeVHWvk8zC+r5esFUKBjBQIh1m6KlL2ewDOWgcpUUPGYQRNc4GGqL7AoQ8R76HucxZS\n/AAAIABJREFU1yPoTRQnSxpeDAnUYG8RxsbIEHUAP/HZAUGvNJSaGs5pPUqYPEsYAiysQoFisboZ\nQS84qJQCaiCoD3vvmh86JPuzYch9bhgCApNJUXmAfC4HDsjn+LOfwXe/K651nZ2zAxjegqDvHEJ7\ndESaKCbCCum5ZN5hytOFriD3BlZQiNbTwAjNDNJgDUHJdO7/agWCXkYQJvYHQvn9XRWzBDs7SRGj\nqBnBSAilplcjRSL2715fT5Yo7+Gr8oWUAMF9+8SorFpDgwGvTBCmnhWaSvnv6ZYln4dpSn6Wybj9\n+4ODcy+EZbNylhqGOIUPDrrvr7MT2L2bAgF6kL4NFZYCz/r1/r/HTGPFBW0YlEiQpEiA0risebX3\nCfqNC/E7i0dG3M8vlZK8y7JE5aH78UgluXT3N3iz+V8TXq92Psuf/s8VhA+1EwkJOzgyAl/5ysze\n5/CwSOyHhipyqcFBSCZJEWOYRkwVgoDhALrZSkNjMTGGK5Xgucg59ARXcSm/kUJuqUguJR/UQstD\nT0ggOJv+wIMHJyYf2azrYKRnDi50aCBYOtpDhqgDBIMNtaxa5XniRReVvW5F7pDz9xMJCOrkoHKm\ny1RGMZPFtm0iLfMLPavRsiSBPFaAkUpB8vkex2o8QHFif6AOD+ivZdxxDlysswT9GKLZDhgHAQGP\nPPLCs2RDu3sBi7QeJh+OQDA0JyBoWXKgrDpdSpLLEORn5QuLihGsTPq1/MrLCOpEUY/LUUo+p1hM\n7v9MRpLFXE7+n81KgtXXJ8nj2JgklZPNHPQLLxD0lSO3t9NPs+sYGnGHyc8mYXSKQYYB69ezAckQ\nGhl2+gSr2TBGJ4gahFeOjtChB1LreYLTxeiom0we7zA7jpJCkg0FGJHwBAMzv3AS3vp68oQ5mX28\nnF9C2q003Hnni6OoNZeYiUxQq3m8QNBvT+/tFVZl9+5y1qe2VhxEJzONsSyXRfSLkRG3r/jhh+FX\nv3Ld148exRkdMUadzAatCepb85ii/pwNNDBKAlnrQrYIZmnRMIJe6bpfwW1oaCIQzOdlbw2FgFwW\nY8fTfDR/C5Ntnxtp52Xdd5E4sgvDkh/47W/PTDbvbfMpK6rZCzBAM1mi5CK1GIZy9uXZSkOVgk2b\n4OSTIRAN03/KpQzTSBy5iPNjcmEutHPoCQ0EZ4LevbLQeNz9u2YFLWuiHflChJYdpTpHbKZIruZ1\nGwLlB8qFF5a9bsXQzrJei98BQffvfv2BldHfD3//95N/PRBwD/T+/mOXCqfTkD7cT85rIFLpGKrD\nAwTrGYOSCwQX4wgJP0ZwsrlyMHkP0cCAfK89e2Su3AvRT2WaMLK3D7BIaSAYjwEzmyGoQ18XlgWv\nehUMrzkbgE3slMfzxaqdJahBgbd6XAmy1q1zXUOVkmRhbEz2Ol0914ZOMfl4OXpUqvv79oksFORa\nSCSEoVNKrpHDh2f2Pi2rvPAyGSM4QAu5YxgdAeVzyZaf2ewkis30l/WEVjsQ1NdqsegvFdTFt+n6\ngIeGRKnx1FOy3vMl359NFDu6SRJD28EbNaEZJYxK2Yql+nosDPKEeRf/TiDtIp2jR6tX/aEB4FSF\n9A0bcIaz67FAlb9vsSj5eU+PqAC810tNjST1AwP+Z2J7u9z7WiFQGV1d8v6OHJF7LxyWfSGbtc2o\ndu2jj1bHMTR0jKMjdMSXJmhowHUOJQypdNUzgn5A0G/983m3ADgyIuunR4GFgyXY8Szrc7upxYP6\nP/lJsXX3xA18g7rCAMEhOWv7+gQMThWplOwb+bzsL2V7qW0Uc5g1WMCganbODJDzaLahlLQonX8+\nNJ1/EgmSIg0lRDGdxzR/xwguSMy0P9A03UQB4Jpr3KS/o8O9gNvbF34z7u2VNzTWmyFIwamCrD+/\nIpOoYARXdTyCUnIAnUiMoN5EKseAzAYIlkrwd3/nAfrFImpogI+/Z4hbboEbb5RrSEt4ikWZW3Ms\nkUpB6uiw4yQZQDrffd/rqac6F2Qdo05P2GJnBKdjALxmEpWV3kJBrg3DkI+ur08qvMf788rlIHVk\nEAOTLDGRANuVptbWmX+f886DK66Q0SaNjVBat4FaxlnHQWGTC8II5nLVd034MYKVsrD6egFTXiah\nu1uSQn2Ah0Ky1tGoPK+zU762c6ck0gcPugYT+lAfGZl5P44Gnl7Woiwsyx4m3+w4R87FMRTcwlYg\nAOsucWUCrfRBsbBoegQ1EJyMEZyqD9iy3HFPO3aUy8xeiM+lcLiLFHHnzFY1kRkzB5EIUF+PiSJH\nhOX00JYpd4+o1rX2AwWVUV8Py5eXg7tKM7RDh+SM3rbNLdavXSt/lJLHnntuYhHg4EFXKVE5f1RH\nb697/msgqJSAyqHuLOm+cc8MQY7ZMVRHIgGNK+OEyaGwbCCYpK9vkkJTlcRMgaBhuHn64KB83kLg\nWIQO74NUko14WJr3vQ9uvhm+9S1pELXtlVfRyeu4j3h+CCMjH9y//uvUOURnp9xTP/uZTAno7PSw\nzB7H0DHqKARqyhxDHVfnOURNDWx+/XrOZCfL6aZEADOd821Pm+84IYHgTKWhnZ3uTZdISN69caP7\n9Z4e2ahyOak4LpSjU7Foa9NHhxk1E65jaCjESRc0lz/5tNPKqM41yZ2okisZqsZeobmEHyNY+ftP\nBwTvuMOVlzE0BI89yjt33MQr3nsKL23cyRvfCGee6cpDQeShxxLplEW6N0naniEYnEoaGomIpgCx\nHAagUFi0jKA+MLyb+GRz5cAfCGr2IB4XEFVXJ9/v6acl8T9erFkuB6muEQqEsEDMn2JxmpomFi+m\nikAAVq8W8NjXB5x0Ei3008wAUTJYhRLFovxS1SQP1YDGNMuThsr1jMeZMHext1eKc6Oj8rg+qA1D\nwKAeIP+b38hZcPiwHMKhULnU+8knZ/Zeh4cFkPzyl3D33bIOZddRTw8kkww7w+QVwRrREs2WEYxE\nxDl082YIn7PJeXw53Y40vJoZwcoewenMQ3K5icWBffsk8U8m5V5av16EMhrgVxrSLHRkjg7ZCg8L\nAwujJjJjU4lIBKipwYomHJVIi9kLWfeXrta11nv3VDLBeFxcvPV6Qznw7emRovwjj7hnfSwGl10m\nr83nJR3at8+VF4KAQs34j4zIuVBpOgaylwwPu6oCLdcdGIBMzxgDNNPDMlKIPaV2Ap4LM+SNYBCW\nnVyHgbR9FDyGMXo8RjWGX3HPrxBgGC65MjwsazQ2Jv8Ij8kFcBri2Mrv/R586UvuBXLFFVIBuuoq\nAP6Eb9HEEIEx6bvr7obvfc///eXzck09+qhcT+GwfCvnHrOrg92sYJAlGMGAc15v2TLz+b+TxiWX\nsIxeG+RamMUi+ZxJd/fCTig44YBgoSDJhGG4UqHJwisLPfVUuc7OO899bNcuUeetWiUH1/PPywE0\n31KzTEY2qdBAD8M0uo6hdfWctKGiXBoICMdsx3J6CBQlc7asF0Ya80KEBgvepLq/312b+vqpCwFl\nfYHdXbBjB+cXH+WP+Y6g8htuANNk0ybKpAEOcJxjjO/tIl0IkEEuzoABRGsmB622PLQRQX5WsXRC\nMYKTHSKT2ctrIBiLCSjYskUG0Sol8p/jVSjJ5SDZm3KSuwClWRvFgKtQKBTsSvmKFbRGxlwgiEUp\nL9lVNRWB/MYIQPnf43FZaw0E9dd6e+G3v3Wf790DYjF3rcfHLGcwt94LwmH3MH/uuZm91+FheW4q\nJX927aoAJ3v3YqIYppEcYQiGHIv52TKCIL9vfT3SXGLHKjrKFAHVCg40SPOaSvgxgt5ij/eztixX\n9XLyySKQWb1agH5Dg3z9uM5ZLRRI9ibt2ZGg7GHyM2UEtZmd2bTEBYL0l+lhq3Wt/dihykJcPC7r\npw2CQABBsSiFnvZ2YfbTaUncdeHoXe8S5ueBB2SvDYelf1Cbx+zbZxt0PCcE0k9+4q+W6u93GeVg\n0AWEAwNQGh7jMKvZxwYAVFgcQ5cvnz63nEms3CJF/gRJ8oSwxqsfCPqZxUwHBMfGBASNDORRY6OO\nP8Zp7IYLLoD//u+JDbeRCHzzm1BXRwsDvIX/IkwORkYpFi3+7d/8gX9Xl1wbmYzbTtDZ6XqCcPAg\neUL00cIgTWWOoV5sMOdobYW2NtZwmAQpLKCQEtS8kKzgCQcEvWzgVE36llUOBDUTuHatW8XNZuXQ\nP+kkyccDAdk4tm+f3/llmYxd3e7tYpgG1zG0vt5fguCRh7bQTyDvltG9YGgxh644eRnBqWShWof9\nox/Bpz8Nn/oUgCUVoPZ2ljDAx/h7tzF561a4444JjOCxaPgLBRh96iAWkCJOgBJGbQJQ/owgOECw\nGbtMWiguWkbQL3Hwk3h4ewQr2YJUShibgwfltUpJ9fbUU+Xrzoa/wJFLFUkN58t7QePxOQPBwUE5\nZOsaDGraltDAKHG7f6yYLzmmCNUS3sHDOir3aw3eyhhBy2Swr+Q4+OrE7fLLJZGLxcAo5sh29PL8\nfz5K755BDGU6kjJvUefw4emNwEol2ZuPHHFZxcHBCvmWMzpCZgiqcNBhKecCBJ2or0dPT15NB2BB\nsVjVQ+UrzWIm6xGcrA94fFzWJBaTvhv9vEJBQIVlHWcpZU8PaWeGoIVhKDACswaCVuMSnuEsnuYs\nShjkRnPOZ1Staz0TRjAWE0ZQ9wGDrPHwsADA3l45t+vqBOBv2ybgzzTd+/jXv5a9YvduAVG6L/yZ\nZ+Szq6mR+9WviOsFgvqeDYfl+w/15ulkJQeQJEwFBQgeKxuoY+kFawmTI04KE4NiSg6zagaCfk7Q\nfmd4IOBKcvv65Bwv9I8SIo8Casiypq0E99wzOepetUrmawNv4Tu0MoBRzEAqRVcXfPWr5U83Tfls\nn3zSHQOh7789e+yc/sABxqhliEZGaQDPDMFzz53tpzFJrFvHKjqoYwwTg3xaPrTfAcF5jJnKQrV1\nLMiNr29upcoXXMuHWlrk8XhcLpj5XDTtGJrrGcbEcIBg06rYhGHpQBkQDFOgviiiekvyhKo9OGYT\nftJQ7++9bJn8f2QEPvtZeN3rpIr4pS9JhTCdLMmJceQwCotPcgsNVLgCffSjnNo8KMmlfScNDc09\n0UinYWRXFyYBSgREAlybmHrMhQME7caJYpFSafExgloqqA1BdPi5vem1sKyJvbtdXXD//TLu4ytf\ncfvAWlvlPtczi+YjBgYmd54c33WEnBkia5uHBEIBCIac63KmoX9XfcC2tgLLlxPAdOTCZqFUdbLw\nmcybqgSCKp2Evj6GvvFD9v3iCJRKDiN4xRVw5ZUQ6O+mpvsgwUKaDlYQSI0RH+3CMCxHgqYTvuHh\n6ffx0VF5Xi7nvnZoqAIItreTtYfJp4iXzRCcLfCfEJs3A8IIKhsIlkoCRqvNHAj8XUO9QFAn95oh\nqpSG6nu3vl7W4KmnZOzPrbfCf/yHJP9DQ8dx9mBnJynidm8oGCG7p3uG0lAvI/gk5/IjXs+zbKZr\nMEJHh/z+1Xqe+4GCysJLPI7jiq6LXsmkrOvYmNRj6+pkXR94wF863t8vZjCaAczlpH9Unw06R9i7\nt/yeKRSEDdKstBcIKgX9gwZdtNGFDCPWjOCxGsXoqD1zLY3GmGMYUyjIf7SKoRrDTxpa6coPLiNo\nmrLeo0dGUZkkYeTJp9KO8dnPTN9Qf/318JrXUEuSt3O79OInkyRHC3znO3Dffe5Te3uFxCmV5L4b\nHpa9oqZG8oSBXnEN6mQlQyzBQhGqCWAYkk9OWqyfbaxbxxoOU8s4Fop8Rm6KhXQO/R0QnCS8bODJ\nJ5fPojv77HLTGC0piMXgLHu2+8jI/B02ySSMdyexlQHCHijF+i1+KJAJzqFLMp1gmU4yPRtb9GoM\ny/I3i/ECtGa7tfKb35Sm4DLmqFSCp59xPqh38k0286zsCN6hX4ODRG75OBs3utdHqTT3PsFUCoba\n+6UxHGTTm6w/UIcNBFvoRzMClrX4GEF9gAQCU5uHwEQZkfc+3Gf7LIRCkjTeeafMgywU3I18PlhB\ny5I6wq5d/hXPwafFnzpDlCAllE1JzZUR1NHSglPlaEWuX6tUcpxDqyX8GMHJgODSpaB6u2F0GLA4\nynL6hoMw0I+RGiUcMjn7lDTXPfFR2LOHmJWUpNx2cUwMH4GODm66SdgHLQ+1LCkYTBXDwy4bCPLa\nfL5CZrZ3LwM0UyLAOLUEIkEH3GzYMIcPxxs2EGxiWPaLgjCCpVJ17gHTSUN1oWQyRnBkRMD7fffB\nP/0T/PjHcpbrucHt7bJnHDd5aGcnaWL2OCDLAYIzZQQ1CLIalziPxUhBQdwEU6nqBYJ+jGClWikW\ncz8DLyM4NOQas/3v/0of15Il/jMn6+sFKGYyAgS3bnUVW93dAgqPHq0wBUE+14EB+R6BgPvzw2FQ\nhRwDY2HZa5DJ8YZnhuB8RLwhRGNLiBoE3WrDmGplBE3TZWqnGgkE7v6bTgOWxfiuDhS4stCV4/CW\nt0z/Q5WCf/93aGjgT7iT1RyR1qqRUXp6TD7xCWGKLUv+/9xzcs099JCMCrnnHskZuruhY3sfFIsc\nZg2DLEEpRSgi9/N55x3b3MiyWLeONo7SyBAWBsWcFHJ/xwjOY8x0dISfLFRHPF7+2NatbpISDkuC\nYprzwywUCnIRZjr6SRMTxzmKUFvLho2VE8btaG0t0ycstY0ENBBc7M6hXhDovTm9bmMaCD7xhM83\nOHQQbC36G/mh9AW2tsru8Dd/U/7cf/s3NtUeKgOcc3UOTSdNhg6NSR8RiKa9ro62tileZFcpmhkQ\n2apVopQvLTpG0AsMvPean4OaV0aUTrsHjZ6np/s9dDzxhMhEtFS0v9+/Sjmb0MOuwZ+dHdolN2GS\nhCMLhZmNNPGGFwjW19vVbTtbbkO00FbJrFog6GUIKgtreg9vufcOjCNumfwIq7FQgEVouJeTd/yQ\n2qsu4qQf3MoWniJGWtgzpNgSIceW/T/g3I4fcfbZ5fvG1q1Tv08NBHV9SLMG3jlU7N1LH62kiFPC\ncIxi6ups4H4sYQPBKBmiZBxGsFr7BCvXvVIaunq1/N9P/q379H/zGwEKfoxoJCKS3+MmD+3oIE2M\nPKEyRnCmQPCcc4TJvvDlMS7iMTazg7UcshGztSiMgabqF4vHBeDV1LhrXirJGZtOCws4NCT3kXe0\nypVX4qilEgl57fbtbiExl5NEf+tWYQJ37RJpqBdkdXW5RjG6PxDs/WFkmAwReljOOHUQCBKMhVFq\n/oBgNApL1iSIkENhChBMphgc9O9ve7GHXttgsJy5nUrVMzYGHD3K2Lhs/poRPO39r5q5M8uKFfDl\nLxMhz1d4L7UkCRVTMDBI96Esf/qncg38+tfyXh5+WHLu+nq57vbuFdzw7G8kN+uiTYBg0Jh/WSjA\n+vWEKHESIleyiiXyedm3jjUvmSxOKCBYKsnmoS2FJ4vRUbefLBBwjBnLwtsYun073HabMEHZrNv3\nMR/JeE+PfJ/oYCdjNBCiKAn/ZP2BOjzy0BV0Q0GuoBNhhISfUUyx6FbIlZI+T7/P4pJzMry779N8\nmRu5l9dxI7ehNm6Uxb3wQvjQh8o9gi2LM3/8GcIhyzkodH/SbCP17AGG8zGyCL0QDpgQi07NCIbD\ncMopLEXm5ACY2XxVsgFThR9DFIlMlH7qe89rL6+vh3RakgZ9qHtlmCMjIiHTQ6ePVUbpZSr91mJo\n/zAWMEa9jHmwZwjaLV8zDu9Z6Khk7F9sFUfk3yXTAcHVIhesdI+EiUAwkQD++Z9pvfVDGJhO/27J\nPtYUJmEKbBn5hdMA9Ca+T5ASrWqAOiNFC/0o4E/5Ouqtb2FLfG8ZENy9e/L3qB3mhoZceZlORp3h\n5cUi7N/PAM2M2bPG9OiIk0+ehyqyDQQV9ggZe6h8tQLB6QbKayCoWX+vM3A6Lb2/gYB7j69ZA1df\nDZdcIs+JxaSP+7hJZ21GMK97BMNuEWAmEQwKI3buK5t5VfRXvJG7uYZ7KVqKYt6saiDoZxxS6eiq\nzZ1WrZJ1VWYRigVG+gs8/LDs/w0NbiGmoQFuuUXqtR/7mPwQI52k1hwh0zvKkz88xIFHe7n7btmX\nGxtdkHnkSLlBVE+P5IJeWWgiAcbIEKHsGAqLTtpE9ltXSzCoCIeZunA7i1AK2k6vw8AiQUr6TKvY\nOVSvdzDono+l0uTjI5SC5LiF+cR2AdvYQLCxkY3/7/yJL5oq3vIWuPZaNrOTf+CjRMgTLqVgeIjn\ntw7wvreN8eSTkuYND7sjieJxd+TQgd05BmjmIGsZow5lO4YqJaZz8xY2ibOWQ0TJOkCwVFq4dT+h\ngGAqJZu/t6fLL7yzA9eudWU/3li7VmREOgYH4f/+D77wBZlLphuajyW0y+f4ONT2Hyh3DK2vn1qL\n7pGHttHl7LAnEiPo7Q8cHnYP/vp62dgrE+PmZvjMkn/mzblvsYnnCFOQpPq3v3UZ1kgEvvzlsp+3\nqf37hMb6nWvqwIHpZ935xei2PYxSR4aYyCDqagA1PUt0xhksYcBhOcxcgVTq+NukL2RMBgQrLZX1\nPaGZMi9jkEwK4NOH+tveBm98Y7naVzs+dnUdW6I4FRC0LBjpkOpigaAwgrE40ejsGSL9eyrlea1d\nOVhOLyEKDiNYLFbPzLGZzJtKbPsFfPCDNGpZJBagsGJ1gEJhESbP2biVmYt5lLbacWLnnc6SM1cQ\npshL+TUbaYd0mrNvfj0hK+uAD+8IocoYGRHmr6bGZaENQxKdwUH7Gjh0CIpFBmhmnHqUMgiGZdH8\nCoyzjg0bnI2ukRFpATDNqpwlqJlMwyhni7xn9dKlroW/fo0uBo2MSAFHn9e///vw9rcLCLz4YtdB\nVs8lm69e4Cnj0CHSxMUkCDAiAgRnygjq+7tkGXDqqbaRWBSTAMWsSL4zmfk1pzte4ccIVjIeumC/\nss3C6O8l3N9BZPAoW/97P/3bjxDrO0h9927Yv5+LRu7nDvNPeOnNV8KWLZz/e8v5o1+/B554nLpD\nz2AMD9LZafHrB9LE8sMOY6iBYCYjbLLe9w8fljNDA0Gl4LrfN2H/fsLkMbA4wmoIhVE1EYJByQsr\n5frHEqvPl6JeAnGetewDrxqBoLe9QxdvJhsPoz/D8Y5hxkcLmBgYtntC05a1s1dSKAVf+xqsXMm1\n3MMNfIMwRZnTWMixc1uKbQ+MUMyXiMfdPUQDwa4uKRTs42R2c5p8y6A4hm7YMPP7eUZh55or6aSW\nMcyC6dwjC9UneEIBwbn0B1bKQnUoJUWGiy8uB4r5vCST99xz7A6dIyOyOWVGshi93aSIORrpYFO9\nUx31DQ8juJIO1AnICHqBoFcWusRut6jsBWtbWoR/+ZfyB9///onWfldfDW96k/PPRkZY1f04ypJM\nNZudmknwi2IReh8XHXyKOCEKGPbuMm0T8hlnECVLBPnFS7YMeDHJQ/2AYE3NREZQA0E/6VhnpySO\n4bBU5GMxIVTe+16XTQsEhDHI5SYOLp5NeOUuuVx5opbPlEj2ph3Ar6WheozFbCIalctT97YBDiPY\nxKCMkLAZQageeaifNLSssNF1lNof/gcgbJj0QypobMSqrYPmFlQ0Spg8Z/GM8zL15jfz9v96BcQT\n0NhEbNN63sW/O19v6t3Nhud/hmGYzs/cts3/PXr7A7NZSRYee0xe4xjG2FXFQZYwSh0q5DqGzgsQ\nDAQcPbE2B7KKpapkivT6hkLuHl7JCMZiUvDwGzCue7o0MPcqZmpr3X9Ho8IcHhegvHcvo9TZ4yNM\njBq5SWcNBEtgbTyNPWxkjAYiZKFYcO6Taltr8GcEfYFgqcSqh/4To7/HHsFhkSVCyMzSnDmC6u1h\nS8ePueXp36PhJ3fCL34hspyeHt7B7ZzBcwQwaaafKBmaGaChcyfkZJOORl2Geft290zR7KAGguvX\nwxWjd0NKTEsUJiWCUFuLUmpe+wN1NJ6/gQRJ4mWjBKyqBoLevxeL/uOBlAJDmYx3Jxml3lZ35FFL\nl3LaxQ1zU1IsWyZa4Le+lQ/zeS7nlwQpESJPkAKh7ChGXzeNdXJBGobcfzq/P9IbYRvn08VyFCaB\ncIBgcJ7GRnhjxQoIhRznUAuLfFZuloXqEzwhgeBU/YGZTHm1RdvK+0U0Cq98JXzwg+I6qZNJTWt3\ndBxbMu7IUzsOMYKUr0IUIBpl9YZwWZ/ThNDNLsBqjoikwjaNWOxAcDqjmEmBYM+T5R9OIgHvfrf/\nD/nCF8psi88pPIYaG3V+/o4ds3vP6TQM7ekjRIEktcJw1IscYiaMYJASUbup3MoXFi0Q9AKDSGRi\nr4Q+iPXB7q2W676tfL58UHltLbz85e7rtBvfsZjGaPCpEznvWuTaD5EyI6TtWZHBoNhNTlnYmSSU\nEoOqMnWADQSXeICg/l2rxTnUjxF0/l4SuWUClw5ebvRjNDdD2K7+BAIYS5Zw+mvWEX/91TJb9fbb\n4Tvf4cprYnzmM8IIf/nHq1n1oT8s+9lbRn5BZNTdMB5+2P899vTIn0hEnIZ37hTct327a2jB3r0A\nDNHEOLWoUNDZt70K82MKe71bbHMgLQWuNkZQr28o5O7hlT2C0agoN7yGUNr4pb3ddRceGZE1uesu\nMQT7l3+R4tzAgGzbBw4ch8/HNDH37WeEepsRtDCiklVO51Ggwzsn9Lmml9LLUmKkxElykfSDeoFg\nZb9YLFSAt76VVQ/diYG7+RtYtNJHAJP1HODv+IS4bFdEkBKf4O9sMJVmGb3UkoRikXD7Ti57qUUo\n5JrBdHbat2xPD/vb5Y3pHsFNpxZo+6cPsIIuGwhaEKkRRtB+znwDwcTJy2kMJYnazqF5M1C1zqF+\nxVzv/FcQ6be+3wPjI2TNoPTjYcn6rlvPaacdw5tYsQL+8z8JPrGVz73kPk6lnQAmQUoEMKnLDxDa\n+TRvfUOSP/9zeUk8Dio5xtGhCM9xBoM0i9okGpgwRWBeIhCANWtYx0F7hISikJbiftXyRlIJAAAg\nAElEQVQzgkqpiFJqq1LqKaXUs0qpv7Uf/1ulVKdSarv951We1/y1UmqfUmq3Uupqz+PnKKV2KKX2\nKqW+ONP3oJPGqRjBBx90L9S2tplp+cNhqQq85z3w0pfKY9GobCpzlYfmcq5jlfX8foYRVipIcXpZ\nKEh2YguX13EQsKCQd1xDq6VXaC4xHSOojWLKE32Ltke+W/6N3v1u/OdzIE0Ln/yk889zeIpgahRM\nGeiux4rMNNI9Ywx1Z1GYZKgRIFhb5wzMnjJs51A9N87KFxbdLMHJpKGVQFC7MHoZQQ0EOzsF5993\nH3z72/DHfww/+IE8Z+NGV1oZiYgqYHh47pIrDQS1C6h3LXLP7iVFgpQGgjGhiGbbHzhp2MCgmQFi\nZLBMq2oZQV+2wB4W5wDBmhqWXv9qVCxa9j0MAy58XSvcfbfQem9/u5N1XHKJ/HP9euBzn4Nrr3Ve\nt4WnCI/2oUryAx9/fOL7y2TkUFZKwPXYmACYUEhASCplFx727qVIgB6WCRiwR0dEo/PXS6QlA8uQ\nIpZVqm5GMBh017pSOubHCA4Py3ocPiy/849/LKTQF74AX/+63Ot33w2PPAI//ak7jqmra6K0fF6j\no4NUPkiSWhQmSgUwQkFqa2cuH9TPGx2FgZbTCFJkC0+LDLgo80GrFQj6MYJlLRVmifj118Fdd7GS\nTqJkUFgoFEsYIEyBVvq4lY8QZ5KNOhBg6foEHznvl6glTc7DW3iK24eu5WPxLxGPy/1oGJDPmdx/\nw/cpLm+j8xftMDSEyqQIBU3O2Hs3qrODi3jMBoI4iF4DwfkaHaEjnlA0NMjcPAvbOTSXr2pGcCqV\nXGOj7RJrmRgjQ4TJ24wghFsboKZmUpXerOLcc1nyq7v5hy/XsqJmhBqyxEnRSh9/k/wr3vkfl/Ga\ni4aor7eI9R9EDQ1QwmA3p5OlBgOLUG2EUAg2bZqH91MZ69axhGF7T1eU8kXdbr4go2+OGxC0LCsH\nvNyyrC3A2cCrlVIX2F/+gmVZ59h//g9AKXUa8AfAacCrga8o5dQOvgq807KsU4BTlFKvnP7nu70e\nkwHB558vd5H0qCtnFErBmWfK32tqBGjMteqo+9eam2G0vZchGwiGKEDdNEYxOuxfYAmDtrV4gVLJ\nHVC/WMPPLGZaRnBggLYez+IHg3DTTVP/oA98wEnCNrGTCBnIZLAssaueDdhOPbqDIZpsUwEI14jj\nwdKlM0gaNmyAUMgBglgmZqGw6BlBwyiXEgWDbt+uThRzOUn2TFNAoK6ohcOSPN12G7z5zZIs6sqe\nYUgSn8+7rLwObWk91WZsWS4Q1Gyut0c18+zzpIiRQjaiQEJYgjVrZvppTBOtraAUTQxLJdkyKRbM\nqhoq77fezlrb2bsDBK+7jtZLNkzoNQmHZ1itDQTkArAX4GyeJkwOY3QEsDhwYGLBQctCw2FRoWlx\ngDYPGBx0gWCKGL20ChiwZ41t2DCPduM28F/huMSWqhIceKWhXkbQTxrqfWx01DV407Pg/Pr6DUPW\nZ/v2+WEFh4enkY/v3UuShA0ELQjJ2JCZGsVA+d4fWr+Ks3maOsZpZBhKrhN4ta21d5SAt8jngMJi\nEfXMM0R++mNA5mRGyLMy1E/blhYSLzuP2isu4Nb/WMqS226Bz3xGBsPedZfMg3r8cVngTAb27+el\nj3+BW//3TN548k5u5mY+z4doo4vIJz/KS07qlj7BYh4GBvnVjnqOsoIRGqGQJTTQjfHYb9n0nY8B\ncBGPEaBEsC6Bsul9r3x0PiMYhGUtJQcEFwhBPsfw8MS2iBd76L3czxxGRzxug+nhYQyzYI/4UaAM\nwqtln5sXIAigFJv//DI+d8/pXLr8IK/kZ9zGjVzJL+Cpp4i89hW8fuCbBI4cIkqGACWOsApQqKZG\nwtEgmzeXEw7zFo5hzEFCFDALRfJ5twVhvuO4SkMty9JlmwgQRNscgt+ReC1wl2VZRcuyDgH7gAuU\nUsuAWsuydJ32TuD10/3sTEY2mpoafCWV2Sz85Cfuv087zUX6ySR8/OPSFvbxj8sUgcnMQFpaXBv3\nYlGqlLM1DtEmMQB1CZPM4T6GacLAIkAJ6utnNn/KBoIGUM8oFAonxAgJP7OYqRlBC44ccRIpQOgi\nPcBosgiH4cYbAVjDYeoZQ6VSgMXQ0OwaupOP7WSIRnK2Y2ioXv4/o3ECoRCceiq16JNBYSXTi4oR\n9Js55TdXLhwWEtcrOenrE/DW0SHFIG0aoWN0VFSDt9wiz7Es+Xp7uwCn4WG5j595RswEtm2bWqvv\ndfjas8fdCzT7MLSrGwuDJHEClDBsR4R5A4KhEDQ3E6TEEuTCN4tmVQ2V1wmhL1uQlIKHAwTPOssZ\nKu+Nmhq3MDdtJBKOCVQ9Y5zEflQhB+kM2aysvTf6+mT/6OuT68cPCHZ1gdUuoyPG7Kp2oCaIUvPU\nH6hjEpfY/v7qUn7ota40i/GCPm2o5L2/NQg8eFDOeaX8gSDIMnd0yL144MDcAZRpihHtzp1TmHI5\nQDCBgQmhEIYxO2OJSER+10gEtryujYQhFaZmBsAsgVWdzqF6PzcM9/NzQb8FO58lPnrUSQzjpNlQ\n20fw3M2E6uOEIwZ//6U4a/7kcnjf+8Qi9D3vgT/8Q7jqKpForVtXNlPivAsMbnzw9bysYYebcObz\nXHHvB4mOdKN6ulBmgb2cwoNcSQnDliQWaCr0siy9H4CzeIaaYInw0gaCQVdW2tw8uYDoWGJlm4WB\n7HfCCMpGWG2soJ/Ko3J/isdtMN0rsl/vF0JRaZ+YyvF/LnHpK6J8c9/L+MLl93Aqe90vPPUUr3/k\nw4TJEydlz541oaEBI5GYeaFxLlFhGGMVTSevXQh56HEFgkopQyn1FNADPOABc3+ulHpaKfUNpZTe\nJtsA7zSmo/ZjbUCn5/FO+7EpQycRkx0Q99/vVlhiMen50wfNd78rxpGDg/L/T31K3AZvvVUYxGee\nkSLUt7/tuobu2iWb91zkoUND8n6jUdj/4CGsXI5hGkSOEAxCPDazypPHObSJobLZQ4t5qHylNDSd\ndiV+oZBUZLWdPiCZxNgYbXgowg9/eGY/7N3vhpiYfpzGbpRVgEyWXE6ShJlG9xOdlAiSpUaqjQ0i\nOZnWKEbHGWdQh9AWFmCmMoueEayUmOhKeyUo6O2V672z03V39CsGZTJyLz//vCT0zz+PAwIOHpT7\nWP/8qT5bLVN79FEZVq8LDnofGGgfJE+YPGHbKCZGKDSLtZ5J2OBgKfZFXmVD5f1kRE7/UCUjuHkz\nra3l7IlhCNiKlqtFp45rroHXS03xIh5DYcL4GIVske3b3afp4cOFggD9UMgtLGggODwMA915MkcH\nPUDQIhiVxHRegaAjDe0hgOsaqtnwagk/QKVZIx1+0tBkUtahq8sFTYYhCeV114kEWBdOo1G5Tp59\nVs4EfY/PNsbH3ZmmkzIz+/bZQDBmM4KzB4LBoBzjF1wAsaYaJ0Fs1TLgQrEqgaCf1B/stc5mYWRE\n+iB1bN7MR/7vCs44N8rGjZJ7zUmSt2qV6IU9ce7B79G0fxsh8hj2vL47eKe8HyyCFDiD5xzwGKbA\nOS+JE44GCQRcSfiMivNziJVrQyhMaklSJIiZFURQbX2Cfnu630igdStyMNBf1hMarIsRCMwjG1gZ\n8bgc1pdfXvZwA6O8iv8jRhrDUISWNhOpq3GKyfNuFKPDAwTrGMcqlhbUOXQqu5F5D8uyTGCLUqoO\nuFspdTrwFeAWy7IspdSngc8DN8zXz7z55psBOSxWrrycV77y8gnP2bOnvOJ7zTXlVQc/17h0WsDj\n/ff7f623V1jFSESSAu/MsulCs4EtLfCD+/oYo5YSAQFzdfU0NKgJRpa+sXat/OCeHloYYDcWVr5A\nqRSumoRwtlEquZVFXQz0soF6Pkxfn2dDOtJBAyPEsPV8r371zKmEpia4/nr4ylc4h+08wCsglSKf\nr2HnTsU118zgPRdMuneJDC1FTGS8dZLczXjA+Bln0ICLTsx0dlExglOah9ihDRhsZaQTfX0i4+vv\nd9lApaS18uDB8j7AREL6O5ubpWi0f78MdW5okD91deIMmU7LteYn281khHnTYLOjQ77f8DCsbisx\ndDhJkrjtGFqCeJxVq2Y+H3dGsWwZPPusU9zQfWN9fZO/7xdT6LX1Sn+zWcAyIV3BCG7eTGt6ovvc\nnGY7felL8MADXJTaSpAiBcKURsbZtk02XMsS+eH+/bLG6XR5Yq8BYakEh3alGaOWflpkhqARIBSS\nRZ43oxhwDpdGRoiQIV2KlrlJztSY5IUOP3BQKSPTjKB3rcfGZD16euQe0sXed77TnR94zjki3tCz\nwQYGpP63f7+c1bNl473gb3TUbTfwhtUujGCaeBkjOBtpKFRIzzZuhP37XRlwoUipFK46IKjX1Zex\ntnX1zv190knw0EOc3NjIbZfMww//gz+QRvE77wRkD34ZD3OQtWSoAQLsqzkTojFUPkUoO8Cmkqeq\nu2YNF733HB641X0oGHQnTM131K1too4xBxjnsyVqqF5GcMqRQAk46dCDUIrKPQNgBAjVSUXvmIxi\npgsNBl/72jKHsOv4HvfUXEds5XKSBbkZQyHJBxYK/OuLaQ2HxTDGwwjOxjn0oYce4qGHHpr2eS+I\na6hlWWPAQ8CrLMvqtyxnO/h3QPcNHgW82ryV9mOTPe4bN998MzfffDM33ngzZ599+QQ9byolox50\nbN5cfrHlcrO3bNX21fv3C7vX0TH9a3RkswJcDEOSttSeDoZpIkhResDsQfIz6i9RyjkJl9oVRHL5\nRc0IzloWmk7B4EA5G/iRj8zuh950EyhlswgWlArkk7kJcrLJIvPs8wylw4Qo0scywkbJGTDunVU5\nZZQBQYWZzi5KRtB7iFQyCF4g6JWODQwI4BsacvuEQNx+v/tduOEG93rRieJDD8nhnkoJoDj5ZElA\nIxFXBjiZkUwmUz6vcGjI7cs1DxxiuCgedmARDFgQCs+fUYwOmyVqYYAweayiOIdWS++Y33pnMrga\nf6CWcXHjWbp0AiOoFLzkJXP4watXw803s5kd1NjjWKxslvYnRhkZkT396FFhfI/YSkx9PWi7cT3M\nvP9ogQ5WcYQ15IhASOzGQyHm5BA7adhrLfZDGTBLToJVTc6h+n72goPKYo8XCCoFFAukBrNsf3ic\nUtFEKXlOfb2waDo2bYJLL5W/6yJve7vsCwcOzJ4VrASCfpFtP0yJADlqhE0Kzp4RnBB2crLSFkVZ\nxepmBL1AwFl3ezEcRvDCCyeOcDrW+PKXy9y5ruAX1JCVnrS6eqhvgHAY1dBI6JwzOeMf3y4A8rrr\n4P77ufCyCNGoe68nEvNvFKMjsWYJjQxTY7uCF7Ky/y1GIBiPQ+v93yJGWtQy9oORiBzmCwoE9Ru4\n7z542cuch1ae1cxL/uxM4k1uUhmJSHFp3vq8K6MMCI5ilUoUChamKYzgTCX/l19+uYOBNCnmF8fT\nNbRZyz6VUlHgKmCP3fOn442ALr38BHizUiqslFoHbAC2WZbVA4wqpS6wzWPeBvx4up/v5yRpWVIA\n0CYytbVCBnmjvb38Yo3Hp9+TDEMOo3xeDuL9+2fuPthpi16XLIHHH8nDvn0M0UgdY3KYNDbMbsOx\ngeAKzQzk84u6R9Bvnac0irE/cAcInnde2SYwozjlFLjmGs5iBxEEiZrjKTo6XGvzqSL16+0M0UQR\nQyTAiTCgUEqmgMwozjjD6QezADOTX5SMoBf8TQYE/VwFn3hC7mPNCDY3y14bj8s80A98wH1+IiEK\ngscekyTv3nvFbfD734c77pDC0dNPTy67S6cFCGpGMBCQ68A0YfSJfQzTQJIECotAjaDFeesP1OGw\nRMMTZgm+2PsEvUYSurBTKtnrbX/oBqYkRps3AxP35UhEZrzOKW66idoz17GWQyIPBbL7u/ifN9xF\nx19+if5b76Dv58+QGxgjGDCJROSauv56eXk4LNff4BB0sIq9iA5Uj45Yv95fmjznsNc6ToooApRL\nJTnfqgkgVAJBLb3Uoe/dxkYIBCyM0UEYHKAwMMzeBw9h9BzFOHKQyDNbuaL9qwQ/fbPYh9qH77ve\nJd8jEhEwnk4Li3jo0OzlVl7zIC0TLYt8nuShAUoEKNjCKxWS/tBjAoK2Nm4VHVJ0tKWh4+Nzk7i+\nUKH3Il/TrYz8Io752UJQbXV10stjV482s4O2xBiBFctQnrFQSkE0FuCUm14N//M/Ujk87TSWLhU2\naOVK+ZNIzDPL74matctYwhAx0jJLMC83RbVJQ/1cYisLPXEjg7r3HtZxkLhd2Io2RqmrWxhXVt+I\nx+HnP5fr44474LHHePO7aolGRQCWSAgbuGD9gSAJSjxODXnWcIQApjNYfmRkZnnlbOJ4MoLLgV8q\npZ4GtgI/tSzrf4Fb7VEQTwMvAz4AYFnWLuC7wC7gf4H3epjD9wHfBPYC+7TT6FThBxD27y8f/H3t\ntRN7SnbtKv/3xRfD974H//iPMld8zRqpUlx+uRSLzj9fnqe/z9GjM+sTzOelp0wDwUwGBrfth0Ke\ncWql+h2OQF3dnIDgattIwLKdQxcrEPSbIeg3TN5xXhqUO8oBgn/5l3Mr83zwg0TIs5rDAFi5HIXx\nzIz6BNOPiWPoEPLmwrZRzOmnz0LWddJJLAm4ZjFmqUTXoXxVmUVMFX7VxMrhw5WMoI6hoYnDgS+6\nqHyZX/lKeJU9uEZX7ffvFzXA009Lv9/OncIEjY/LY4cP+79X3ePr8SlwihF9Txyx5WL2MPmYbEgL\nBQSbGCJKumyW4ItdFq7fp9cV1pkn5zGKUeAAQRBJv2HIn6uumrwffNoIheCrX2ULT0myDaSLYf71\noY185EcX8z8/raP9UASGBokNHEEpize8Qa4hcKXHI8kgh1jLYdYCLhCc1/5AcLTQAUwxjLJMTNOq\nGvZXh04QNTjwGx2he3yXdD+HMeYeqoM0Y1AiYmVQmTRX7/mSNPNfeaXczJdcwpqv/TWvuWTEYf3B\nvccHB2f+WeVyArqCQXlPpulTFDpwgKQVI0+YEgYYQYyggVKzl4aWhU2JNDFMhBxWsTrZXz9G0AGF\nlYzgvMsl7Lj0UuntedObMP72k7ziU5cRqY2UnQuGIS0E3r1cx/ve5/abX3nlwklDVdsKVtBFjDQK\ny9kTR0era1awn9y/spib2PEIZDKcxH4CmCyNjLJsfZRAQPbNeS2gTRXBoFSIr78eamo4/XSZ11tf\nL4XmaLTMgmP+QynnglpON3GSWLZzKMx/Mfe49QhalvUscI7P42+b4jWfBT7r8/iTwEz94AB/IOil\n1s8+21/vqxNIHWecIUWk886D88Z+Af33yi7w2tcCkhxef70LBIeG5KDp65t8bpS2tS8WcazF77sP\nB4VaKJEstLYAanYWxeecA+Ewa/N21mqaFDMFent9drZFELOaIVjIQ04OnTaOym7vmSc2q7jsMjjn\nHDZt38VuTgMUua4Bdu5cxWWXTf3S/icOkeEU+mmR+TQNMlbAK22aNoJBVqwOEDhYxMTAxCDZl6ar\nKzx/88pewPBjBCvdeCdIQwtZGMsw9EgPI8U4yoxgkCXceYQLn74FvrhHSnz2n5uaVrBb/SWHzZXU\n1hqMjgor2NxczjbpIsOBA/4jZvQm7T20+vsl6enb2UeSBCkkEw0m5JvNe65TOVTedBnBagGCXrbA\nMQ1JTTSK0XHxxdK/Z5ouqJ9zXHopL7n6Cb71MxsIUkOaKEEK9NPiPC0+1kWsI8sf/dFG6uuFHUil\nQOWyWNkcz7CZo4h0U88QnHcgaLvE0t/PEuxScalEqRSsKnBQyQhWjo5wirRf+AItuyMYXF32eoVF\nlAyr6OBU2t0vFItSyXn0Ua6P/zcPnPE0iUQDIyOCObZvl2Ny3z65z6dLNjUbWFsrxYZ0WpLyMoDn\nGMXEKRF0RkfA/DCCDYwSJUu2FKVYEIv9/v5ZtBK8wOHHCFYCwQVlBHVcdZX8Aa7cA/9+pztuCOTW\nmswu4NxzZWZlf//CvkWWL2clHShk38uWYtIrrQwOHpxjL/QLEDNR9cQf+l8A1nNAHmhtRQ8VWHBZ\n6DTxN38jU0r6+uBtb3PnDi9YrFsHO3eykk4i5EgVi+TzktjOdwHgBekRfCHCDyB4WTq/G9my/IEg\nAF/8ogDAf/5nsRj9wAegVGLNGtk4tJNcICDgcPfuibpe7Sy5e7fcJE1NwigWCtBxsAjt7ZQwKGE3\nv7S0YBizZA8iETj3XFbS6ciciqkMo6PVJSWZaVTOEDTNchq9TBrq0fe0cVQWbq5DYZSCD36Qs3ja\nYRFygykefiA3YQZZWYyP0/l8DhPoo5UQeVS9ZBSzAoJAbMMK6u0REiYGpFLs2TOH3+VFFpY1jYuk\nHToRaxnbj2rfBf19kMvSk60lXQyhzAJBM0fYzHJu9jdCv+/YIQ2BP/whNd+4jU/98qVEtv4Ko+MQ\nSxvzxGKwdatIxFMpYep7euQe1ey9N/J5t8e30sWypwdKhzoYoUF6xoBgPIphzONwcR1235geKk/R\nZQRf7NJQ/T4rZYGAQ73U2g65nHWW85z168UUcM0aYdOPNa761ltpi2i5tUGAomNpHiZPK33UkONN\n+z9H/X3fASQ5DBdTqP5eFCb7OJlhmgCFEQ0TCCyQhMwB/oL8qnGofOU97scIcvvt8KEP0YLXVVBh\nGAqFooYsV/Mz33lUAM2pw7zp2b8llEs6wHLfPin4ZLNS3JkudH9gXZ0L6iY4h9qjI/ppoYg0huqj\n5ZiAYFMTNDaSIEkNacCimJfPoRrX2p8RFLOYBWcEK+LUU2Xv0NecliI7OZ9P1NYyc8+GuUYkQltD\nmgAl4qQoEqCUEQRVTX2C0xZziwUSjz4AwDps3evSVufLLzQQbGmRtP8735mHQuNMwmZ81nDY7vN3\nnUN/BwTnEHpQqddJEsoBQlPTxNfpGWI6IhF7bf7hH8qbikCukDe9CdJpxykyGpWf2dVF2VDiVEoS\ny61bRc4RDEqhTw+nfOQRRLNSyNPCgDgLhsNQX8/q1eWyxxnFJZdQz5jjillKy9W0GA1jKs1iRkfd\nDSiRkAquM6dxrAIIal3vXOO667ikdb+TLOYJ0fdMD5/73OTNvebWx+liGaM0UCBIOKIgGKK+Xg6m\n2UT8lJXUM4KFwlpEQFAnC4GAj4ukJ2pLI3DjjSy57AwCQ25WZNqFFIVFmDxn8QxR/KsgazjCB7Kf\nhYMHiWz/Lc19zxEtjdPTI32GDz0khmIPPyz3cCUrqY1iNKugr0PDgMF+k/yhLkZosFfIwqiNs3Ll\nAkhebGDQzIDDCBaLVMVQ+UmNJIoF5wNPkHQ3TjtWrYL3vlcmuswH2AotW8JbPrmBS87Ns3YNhJvq\nCbc00LoqyorgAHHS1DLOH/BdeMc7YNs2zl07QPC5HRhWEYVJNyvIEEXV1xEKBwgEFog9sIF/qwME\nhQGuFkZQF3uUKpeGljGC3ful0Q9sIGgCChqbUMuWEVi/hvAl53PVz/9Kenve/nbfpqI/ynyT2mcf\nYUks7Qw037ZN7ueursnNX3SMjcl9/oMfwH//t9QmKl+T372fHBEGWUKO8PwBQYAVK4iQc1hxs1Cq\nOhmw33DxYhGZjWhv8nHsoa/TzfOdp1BKhF3BoORYeizEVEDweEVtWz31jBK3wXEhLZ9RNQJB7xle\ndn72D5AoSsK9ngMQjUnSZseCjY54sYbHMEYbvunPbr79H04IIOhlA70Hi/fD9DOAqewP3HiqReDv\nboa//mv/H/SjH8Hll/Oyjb3U1rpSllJJDpqODiEgHn9cqvKmKYqe8893x0sMDsqBpH94vXaCbBFZ\n6JyaZS+5xJ5mJJtIMSO9Y4uxT7CS+fUzihkYsDejcSnj1jJOLcljHwoTDrPhL17LyYjzQJ4wZkcH\nj/yqyPe/7/+SzK+fYJAlDNGEAtsoRt7KbKuM0dPWUo9kJIuJEdQHSDBYDv7KDJj6+qi9/vfhttsI\nlnI0M+Awszo0ELyQrVP+vFfyM17F/0l22tcHTzzplPy9TPOTT05kAuwRWE7ByVtb6H9+lGQ+ZBvF\nQMAwUZHQ/PcHgrOhhCmKiZBpYllWVQyV92MELQunPxBsILhx4wQGf+nSWYxcmUG8/HUJzn3DGq7/\nq+V8+556bv5iI6dctgy1eRNKKW7iS5Kc5XJw7bWc/clrMfIZmfmKRYYoxGtRsRjBoDAOcxUdTBn2\nei+zxwpQqi5woCvdwaD79zJGcHiI2IP3OCixmQFpl2hshHDYHhuhOPv8MEuv3CT9GbffLn0ZR49K\n77cdCVL8v9w3CO16hoaYZFaHD0uRRxdpfU1McOcG7tol4G90VMZP5fPO1AMAkntELjBGHXlq5hcI\nLl+OQvoEYXGwv2CDwqyLDOKkROs668r33OMVr5Bise41bmvzJwmOdyRWNtBgO4daQD4tCLpagKA2\nfvL2fUNFMbev15EDJ0hx1ma70IPUcxZDi8uswgaCy+ghRspx/i4Wf8cIzin8ZKGZjHsRhkLlcwN1\nlMtCLc5o/6E0oE8Vjz9O+KUX8qqzuss2lH37pH9FW9ivWCHSv02byt/Xb38rs4HYs4cN7ONxPU3D\nFiTPqj9Qx8UXE6TkgASrUMQslhYlEKw0i/EzinEcQ22K1jGKOVZGEAi/9wYuD/2WekYxMUgXQxQO\ndnLbbbK23mQBIP3oMwzRxKA2irHn5cylETm++SRbGqoooSCVZN8+a4JFc7WF1zxEJ4lKeYBgqQh7\ndlObdqmupfQ6lu00NMKSZozlywlv2cRFO78pO+nBg4Lmfv5zcYS74QanevN+vshL+bUNJi04fAiQ\nRFVbhg8MTBwNU+kYeu65rmlJoWeQDlaSsh1Dg5EgoBYGCNbXOxvLMnoAC8uUa2Fw0H9494slJp0Z\n6XHkqGW8TBa6ULF5M3zsY/Bnfya+W3/8x/Cf/wnfurue793Wy5X8wn1yTw+1z/NcW7kAACAASURB\nVP6WU2knTF6ASk0MEnGUYmH6A3XYjOByulFYWEXTcZOsZK1fjKGvx1Co3CBIKaQfak87UdMtBLSo\nQYzVK8VADXd+4NVXMzFWrIDPfQ7+4i+ch97A3ZyUfY66g08TCsgG+dRTosZJpycf+ZRKSVI7MOBK\nv3VB2VsUSu7rJkOUDFHyhFGhoHMmHfNcR3utHRmwvdbVBAT9egQLBcqQQYz0cZOF6li/Xkz/DEO2\nz/e//7j++Ekj0LaMpfSSIImFopCVzfH/s/fmYZakdZ3v5z37mnuek0tlVVcv1U1XdwPNIjSMD6tc\nGRAZlcUFRX1QkQtXAQcbr6MwIG4z43PVQdzG4fG6XQEV8cqIygUXuqFBoJreq6u7qroyq3Jfzok4\nEfHeP9431jwnM09uJ05mfJ8noTI7MyvqxImI9/t+v7/v9/z5nVcJ9BLuvTyd9u9HUgZOt2nC4qI/\nFwq8+0NjvPzl8OIXw3vfe8D22zhCE8EUMMEs2DZS4iWH7ieOLRGMqoHt3mS+IijhkUc4+7e/Gv6G\nSkV5QwKBBQBcuMCrfu3liNUVikV1U1laUr/v+utVqMGZM37/FKhd+k99SqUR8thjYBqsUuUcOrJq\ncAjY5dpnchJOn1aF9IBEYi+vHUkiGD3X7YJiLl/W36hXHNNcUquI/RgsGh5m5pW38wz+jRQ2F5nh\nykWLpasmd98Nn/mMUoQvXICNdcnSvQ9xlXGWGVCK4IiyQuyGkxZuPkUx0yKHqRRBy8JYanZMt+wX\ntFs05PMBXrCi8tu9uTGgPmgixkYU+88XIJNFZLOcvCHHiVv1cM9116mUiJe+VHVE/fZvq12C//pf\nyd90ivfxn/gkr+J3+GHeN/82fuQ1VyiV/FRIy1LnMoj5efW2klIpBZ/6lB/gkF+a5WFu8jsEi4ot\nHshaR4hQcmieJtKyPXtonK/9TmExblAMaEUwet89IESfDUKoHsDRt76+befos/iSUgTzBRgcwK2C\nOVAiqM/1EMvkMSBQF9IPBCGo+rv3cE8RXFsHo6mqMTTG3/e/kxocJJfzr8eBgS2af4RQ8/xvUtl0\nGWzeywfINZYZmz0HUtmuPvMZdW++dKm9KriyohavwbnvjQ2/JxSAtTXW5ta5yhjqaSvIlzNe31xw\ndnhX8GzA+sTa/UcEO272NP1zXGb90IkgwM/9HPzDP6h8obYbC73A1BQnuEiRBikkpqHenKur+28T\nPAgEiaBL/mw7cI1dvUoGixx6R+jpT2f6RTdx993wsz97wGE8cUXgH32SJ8GxcRzpcuZ9RUIEaW8L\nDRXJX7kCFy9yKwGv6MAAfPrT8IY3wOc+52eHa5xcPcfTz/3fFEXD6y66/361gMhm/cjpe++F3/ot\n9XHPPXrRe//9lFjnE3y7+mW6Qfeuu/bAVe66i3HUUKBEYC+vH7kZwVZLva7ptP+w7dghuOpv305z\nSUVvtcuI3gWe/Z6XM1Fc4Tl8EYHDBkUWLqwxN+fPlJw/D/d84jJfWzrhpYVmhE2qUubMGdVT0y0K\npRTpsVFPjQT17+x3e2g7YpDPBxZja+oPAzooh1e+ktr7fpx0tYprLQG1yHzhC7fZWRweVtvADzwA\nz3oWJRrcwGP8Oz7HGx7/EM94ht8TB5uJoKs233ef2tT5nd+B/1e7TMU1Nde0QYkUDpmikgj2tVw8\nCL1gHGaJAoZnIYN4x453VgQj1tBDIoJb4oMfVGFhATyLL5Er5RC1Mdz332EpggOsKPtYoFS+HwhC\nO0XQI4INJf17RPAVr2D8h1/jVUm4Ef4velF7Z4+HVAp+93e9ZOjTPM4P8zsU1uepNtWLNDurZv/W\n1sKbiC5WVtSmbdCtmM+rJYJHBB95xAuKkaiDyxfUDWPPtlDwvM+TKI+3tPuvVL7djKBpslkR7AED\nEEJZuONgCfUwNcUMF73k0JbhK4H9YA917+XBGWDHCVi/52b9JGhQ6+rjjkrFUy+u4zwgkS0b00wU\nwV0hmiQJ4aCYdkQwVCR/8SJTXGZIWysZHlZFtW5j8cCAapl+y1tCv+PVxp9RfPiryrqGCpv4tV+D\nX/oleP/74Vd+RdVEhGZ2bJvRB/+ZR7kBUycLMl6jXFb5NLuWx++6S1vEAAT26nrsQyO6RTQoBrao\njghs6U5xee/zgQHc8bwSb7+7wrfwv7iTr5DDJL2xzMayyb33KvIwMQGZb3yNa4xxjTEEDrliGoTo\nOi3URaEAqXFFBL2k2dXVUFdmP6JdeEguFziF+g/eg+QVr2B8MhtKHEyl1M/suGQ8lYJ3vCP8td/7\nPe64ccNTIGDzPNGVK+p+8+ST/r7CE0/AU5ccuHCBAVZwSKsZwXLeU5cOBJ5KtEiWljdjAMriFle0\nO98t09msCB6CNXRbpNPwh38YSpQ4e3KN8rNvIZVOee8Tlwi2qyjaF3jnekkRQVv2PRH0rKGNSIrk\nLbcwNhZOFC0UNu3FtkcmA3/8x/CSlwDwXfwZd/BVhucfIZ1SL5gbJNxulnZlRTlKgs8Ylwiur6tj\nth94mA1KXGVcdQhmMt7376lD0IUm/VN6HtQNBlpairflO4iOVQIBItgrRTCWmJpikstkaFFiHdvy\nFf9+IoJtZ29bJiwvh4ng619/KMcVe+iNkBt17oSje0MTIrgLbKcIttv58Wyh6+uwtsZZAo3Uf//3\navgniGwWPvxhZWbW+Gb+P0bNp8jPKVnXMNQDZm1ts687k1Eb3D9w4+eZaT7IQ+jIyEwWhgb58R/3\nicyucNdd/oMDgb3aYG6uD8zlXSB6nk3Tn9tIpXyV7dIl9j8xNIIb3vNd/MiZf+Rn+Xmm3QCHxUVM\nU71NvvQvJnd95TepssIqFVJIclV14LstKs1kIFMfYZBlJAIHAaurKnyoj9HuISJE4BpaXaNIQ6Xr\nAtx556ZSebdEuivu8LrXqfQRF6ur3PHIxzwiKISqkHAtqpalFOj5+XBCcToNF750DZYWWWZQR9gI\nMgMlJiYOKDwEQtbQDFbILripADtGaLtInF8JvQEqA2k/YavXGBhQrpC774Z3v5vcvf/EM56bJ5tV\n12Qq5asMWypWe4EmB4MsU6Cp+mL169gPyaFBIrjJGholgjfdRCajZuzTafU9N93UxV5eoQAf+xgM\nDZFC8h4+RMVeZqSlGLOU8PGPqwmNYJhFq6WWA1FFUIjwnOD618/jIHRQTH5/g2IgMCM4v8kG3A/n\nGvxNnk11QI3ezgjGFtPTVFljmCXKNMD2qwTOn+/toe0E7RRgD3rY35sPvP32Y+oFbQP9OlzH4wDI\nABHcz9nQhAjSXhH0gmJmlWzmEcGXvlS1z7eDEErq0xHXWSy+lb9hwLyq0gelw5e/rJ5B3/iGeohN\nT8MrXwnvfCf8h/8AxU//BR/hR/zfOT7Gc56b2ntvyW23cTLnWkPBtiVXn2h0TEfrR2wVFDM8rF5v\nKeHSJbnZGrqPiiAAmQypX/wFnssX+RNexzSXEY11aDZwmgbv+5FL/I+/HuNhbsZCMYbcYJFyeW99\nOYUT4wEimILVVR57TIaSuvoNHesEQKntjQ1/PlAIePrTqdXCszj6y92RrnxepYQEcNOffoBySXqL\n+0ZDhUyAXx3hLsaCi8Wrjy5zjTGW0bsR5TKZg0oMdREolc/QQto+OYgzEXQXOCHb2NVwPn/lpsl4\npQcMD6u24V/6JajVVJ+g7pHN5TY1Xew/9LmuskaRDXAcHEddJP2qCHr1ERuKCHrWUC2r3n232qt5\nwxvgV3+1ywqWwUF429sAmOQKb+PXKc8+RjHvIIQidJ/4RFgVXF1VX3ePK5/3/85m0y+WX3vwEksM\nI5AYFMjkM9737YsiqK2hLul3Q6CgP841tLd/GwabFcGEEChMTVHAYIR5SmwgLdu7TvpJEWxLBPVG\nj6cIdtubdZSh3/8jLJGlBbY/57+pu3QPOBZEsJ1lcCsi6BfJ+6kK3nzg933f1n+ZEPCbv+nNIbyK\nT1JmnWnjUSZWH2J6SjI8rH7tvfeq+96jj6pxw09/yuJDHzujIsc1iidGede79mHNk8lQv72uLGII\nbNLYiyt9s4O4E+wkKGZpCRoLTe/OVKTBUNk6mJvPa14DL3gBJ7jMr/EOprlEbv4peOopjBb8Om/j\nXrQSKVLkRqs8+9l7CxPI1wbJZQUV1hURtG3s1YY/79qHaPcQ8f4cLRg/cwaqVWq18DUjxBZBElvh\nR380NDuaeeQBbq+cD80Jfu5z6v83NpTl3L2mvB/b2KC0eoWvcxtLKElADAyQyRzwhndAOchihWYE\n+8EaGlQEzWvhp17l1oPy0+4PXCLo4kDnAwG3ryiHqRdUErslvYTLuKNdWIxlhWcEg4ogKIX1F35B\njWnuKk377W/3UoK/lb/hBa1/oGqqFyudVgmiFy/6m05RW+jp037FXaGgSOPKikoMvco4OUya5MmX\n/Rv6fiqCw26dgONg2/1D+qG9Imhs2MomqFFONf2kreMObXGZ5CkqrOFIqezyKCIY9+TQds9wT4TQ\nKrCnCO7qYj6i0ESwSEM5PQ5ozv/IE0EpNxME2/YHu4XYHMzhFckvLYFhkMdQBZelkpLttkMmo1JB\nXvhCprnMD/L75GhRXHyK3LkvkzHWEMq1x8c+Bh/5iOqj/4W7V/hK8+bQ7/nR9wxTq+35ZQBg6Lk3\n6V1VgUUalpePVGBMdBa0XVDM5cuE5gOnuYR49rPCPsL9ghDwy78MwLP5Em/lvzNpPUFFk5YFhvka\nt0E6Q3qyRjqX2bUt1EWxJGB0lGEWkW5QyupqXwfGbEkE9bn0iOCddwLK7h3c+EmnlZjfNSYmNs0r\n3PHwn4c6Se+7T/3/3JxaiNl2OLCISxfJYnGZSS4yA7k86dIBzwe6x44q385ghUrl46wItlULFsLM\ntXJHvBcLN97oNf4A6r14oERQiEC/nE6H7qMQkaAi6IpCrRakHMv7j0Ua6hv266IZH/fm+gXwTn6V\nwSsPkU4rxX9tTY1yuBuKQSK4saGI4uc/r77uzgmurMDq+WtcZZw8JgZ58hV/I2lfiGCpBAMD3jwo\ngGWoG2I/nGsp/fu3e94dB1prvhookBRmxruUeY8wMhmo15nhIgUapHEwG+oGubYWzryII7Z09eik\nWI8I7qos+4hCE8E8BnmaYNleJ2NCBLtA0C7oLtyWl/03YbW6+V7jzQdeUWrgLTxAGge+/dtVks9O\nUCzCX/4lnD3Lm/gof8D38yo+SXblmkqNeexRcPRVYRjq83BxIc88s8GrX7t/N8LBF96hbEOgwkSW\nV2IdI98tospv56CYiC10n+cDQ3j+8+E7vgOAN/JHvIjPMq4jYlJIKFdgaprcQBEh9n4ohQIwNsYw\nC4Hk0P6eE+yYIgmwGlEE9eyuEP4MUTqtxg52XUgb6B8DuOPrf0jOaXh7Bw+rOW4uXVILMXc+UAiU\ndVWnMhVpsMAwDAx4JPFAFUFNBIsYSk2xHRxHLbr6TRFsLofL8KrPOnOIR9Q9hFB7huPjaqPxppsO\nIeTUI/56B6yPisaDRNDtWm21IGVueN9TpKHUgv0kB+98pyfdj7DIi5ufomKpFVY6rSpGL19W64XF\nRX8+8O/+TlnCv/51FRmQSqmNR3thmdUVh6uMk8VkXQyQr/rS8L4QQYDJSaqseSqpZaqOsX441y4Z\nyGQiNmAzPB8oTl932IcWb0xNMcMTCNTr02o43jo27sF/7Yig9wyPWkMTIuhDE0GVFrvuvYCOs78V\nEkeeCLabDwzunrQLijl3DkXSriq5zJsP3M4WGsXwMPzt38LJk5zkSd7Jf+FPeD1vkn9A9Ylzqi/i\n/vvhX/9FRQsGVroFmrz7Pel9HYOpfPOdlN0HBxlYX2P28Y1tfqp/sJU1NFwdcXCJoW3xwQ9CWiVF\n/kd+kVNcoJo1mLxlgMKJcdLZFIODao2zp0AgNBEcHWWYpVCFRD8nh25JBNfaK4KgZode8hL1oevD\ndofnPCcUN/o0vkFp/knv2lxYUBayK1fCRBDgruKXvZt3mXVEOg3lslcyfxgzgqDmiXDUwsGy+kMR\ndMmBbDaDGRKAoPKs+M+RvPnN8JM/Cd///coYcGChQC68fjk9C66TBRcXw9dOHOGe61QqYg0N9MoV\naey/rDozE3quv4pPUrn2OCBJp9Umz4MP+iRQSpUIvLKiiEw+r/48O6v+DWsPX8YgxzplQHApe4pc\nzn+I75sDYGqKLJZXmSNbSinoByLovhdTqUhVSJIYujWmpqgzSw6DDJaXIAnxvp9D+7AYnwiq8+4R\nwcQa6uPkSU/BqrAK0tZW8EBdzT7gWBLBHQXFXJv33rVnOafSA1/2su4PYHoa/vmf4dWvVn8fS7yZ\n/8Gf8jre2/w/efPch3id/BNexSd5KZ/h+fwLL+Uz/LebfpPJN+xmqKkzilPDVHRqnU0aB8HsFy/u\n69/RSwTVXyk7dAhedDZZQw9UEQQ1t/ahDwFQosF/ftlnKX3zc8hNjDE5qdYixSK7ro0IoloFxsYY\nZJmWDqFhbY0nn5CxVoG2QrvdRNNEbdbof5TXIfjMZ3rfc+ut6qW/8cbdJ7F6CKiCeUxun/sMwm6R\nUmOYfO5zatG4tuZXVaRSknc/+mPUmfV+rj4hGB0VDA0p0l8q7fG4tkIg8XSEeZAOUqpgibi+F2xb\nXbvBRaJ9ec63OQPZcpbcQKFHR7hz5HLwxjeq2p9DWdsEKiRymGD7NqJ+sY4FCavjgGj4RLDExsH4\na3/qp7zF1u18jZs2/o28te4lA//N36hr+/JltcHzla+o+7X73/N5lTCaz8OV+xe5pucDLzKNyPk1\nNsPDcNtt+3TM3vyvesi587/9MA/azh4oJQij9x2Cscb0NBXWGWaRNI7XKQehJU0s0c7lYdsox4ye\nCy2zrnZX3MHbBOqmoq1Mg+6mj7aHJopgF+iWCBqGCm9x00JBB8W88Y27t6RMT8Nf/IUaCNQntYDB\ny/gMb+Kj/Bgf5p38F36GD/DB4V/hZ+6W3HzPR/et4NxFKgWjdfVvkIBDitmv98EW4g4gZZgIrq35\nnxcKfmz7pXPLoSfR9MDa4Txw3vUutVp48EFO/K/f573v89+QrrK0H0RwZgYol0nl877n3rahscFD\nD+399/cC7RaJrRabC8avvz408Fuvq0X4T/7kPuzEf8d3eGl9AE+37yN38VHE1SuwusLn//gi93/N\nf19ls3Bb9QmGHv0Sr+NP1ReFoHiqxsCAuhYPfMM7n/csD6Pe3Jgda0WwXWiI+dQ8Aj8NoTqczA21\nRbRUPlArEHci6C4Qg9e4lHhBMXBAiiCooLDv/E5AWbBexSepLKkN0nRa7eNubChFcG5OKYBFP8+N\nQgEuXFDX/JVH172gmPOcJl/0l1gvfvE+jqJH1V99rvtJEdyUIBlQfxNFsA2mpkjjUGdOp0Bb3nXT\nL0Rw0zO8EVGBT51K5kKj0LuIg+ihQMvCcZIZwa7QLRF88EGwm6b35PSK5Lu1hUYhBLz2tao34h3v\n2PxEuPFG+I3fUL6TD3xgc4LNPmHstJpxdJNDZx+LqTTQJUxTLRzcNMeoGuiSrUsPhVfA03fWDy+G\n/vRpJVEBd93ltYwAqjJiP/qxr7sOEAJZq1OioboEoa8DY9o9RJpNQk+/AVZCtlAXlYpWSfeKbBbe\n+lbv0zv4KjlMhGXCxjpf/ssLnP/rc4iVJYRpkM1KvumCIoCv5FPKujo+Djn/RnSgQTEutEpU06ok\nVn8QwaBFsDW7QIpAh+D4QcqofQyvQmJVLRQdnwjGeaHoBhil0/45B00Uooqgro7Yd/z0T3t//BY+\nzdD6RcTaCqmUZHlZkcG5OXjgAfW4KAQE6UJBHffcHMxdajHHOAKbi8yQL/uLWt1hvz/Qm1IT7nWt\nieD8fBuCFTO4xxesrpKSpEx+O+hzPsUllf5uOX2nCAaf4aZJiPxXWEvmA9vBq5DQxEU7PRIi2AW6\nJYLnzqE7/9QO9FnOqVV6wHK2J1SrKiL03nvhu79b7UR+/OPqCfPWtx5g47DC5B0qgtQlgldmBZbZ\n/2WCW3UIunN3q6uwOuc/bHKYjN3Vu9CJ7/5uxf1/7ufUW2I/douHhlQggTNeZ4R5DPQbf6V/iaC7\ncAh2IRoG3nwg6BnBNkRwX/FjP+Yttm/j6+QxPILyJDPMOaOIxhrpKxdJf+kenveVDwNK/X8tH98U\nhX4o6xx9vBMoh4OrHMTVGuouFEKLxKvXCG7VVKb2o4ztCEKf60GWyWGFisbjvFBslxgK+rrfOOAZ\nQRfPfCZuWe8Aq7yEf6A0/wRcvkzaavIXf6Hmy9fX/X5IF/m8unefPw9iaZEFRrnKOBZpLyimVlNW\n9X2DZw1dIIfpzYP2kw14KyKYWEPbQBPBE6gUaseW3rUT1409F50VwYgKnBDBzdDXgWcDT6yh3SNK\nBKM3yrZE8IofpXmWc0oN3G/V6M474Q//EP7sz1Qa6V7K47rAxNkxlYCqiWDTzvDJ/xnzJ8cOsPOg\nGD8xdIrLiOcccFDMNrj1VtVvF+wc2wvSafW8cMbGGWPe76TsU0XQTblMpSIpkk28xFA4JCI4MqIS\nf3/mZyg/5yw385BnWWyRxSGFQJLFpNa4wGke8370tU8/T24sTGAONCjGhV4wRolgXBcO7kLBixaX\nEnltPvQ9lZNtEr4SeOd6iCUymCFraFzPN7RPDJUSbNMCy7/oi1n7YOeH3vte74+v5q/UPaVlkr72\nFBf/7RrnHzQQwreFutWz7pzgk09KKsuXmOAKT3AdaWwyFfXNL3nJPi8hPBuwWyrvB4fE3R7aVhE0\nzdANvpxqhKz4CfBej5M84V3frhMqzhs9sLkuBPSaLWAN9cY7EoShieBYJA06UQS7QJQgbGyEawaC\nYQ1SwrkvrIXIwq3cr6SbI4LBIcFkaRkJ2Cjbyu/9fvxvJNshqggGLxI3GfbShbAn7lCCYg4Z6bRa\nI8ixcYZZwES/IGurzM06+7qLdBhoNzMG0Fi3YT1CBPdLtd8K09Pw/vfDPffw3P/8GlLj44hCAYS6\nlQokOVp8E18IqVhDP/Fm3vAG/ysnTsDZswd/uK5KNOaFSqjk0GYznhayTfNDi4s4wW3kTIbq5A4r\nfI4bAopgVvdGuovtON/fg9e4SwQtC1KBcvE8Bqkbrz/YDdMXvlBZNHI5budrnOEhMlgIIGuuYl98\nClZXKRbVsf78z+N1/BaLqsvvCWsCkxwXmSYvLERezfnvqy0UPCI4yLLqFwuQ/rgTwba9sEsroe8p\nDRcObXO8b6CJYI2rVFjHsaWXAh3n6xvah8UoIphYQ7eFJoJ1dx64lSiCXSNKBKO20OAu3ZUrsPSw\nfxct0OT6b545pK37w8HQENw4tkQaR3UJAqvXTD760R4f2B4RPc9B65tb/Xjp3suhbcipgbUjt+uY\nyaj1oCxVSJeKVFjHJqX+3RsbfacKug+Q6PzQxpWVgGwE1cmqvyo7JNz5zRWyQxXE8LD6u4eGEYUC\nWVo8j3/1v3FyEl7/en7gB+Duu+GHf1hZgQ9lnaPJQYV1VSpv++QgjvbQTbaxa9eQwcdUuUKlekgz\nvf2GWg2E0IpgC6TEaqkXst8UwVYLUi1fLThQW2gQb30rPPAA4g1v4NV80ou0V+84SWrhKvnlWV71\nqnCQeKGgDvo81/MEJ3FIkS+on5yePoDRRv3cGmKJIoaeB1X3w34kgs5SmMmU6wc7ItOXGBuDbJYC\nBuNcBSTSUapgnIlguyRo0Nd6NCAoUQQ3QxNBd85f6hnB1dX928w98kRQSvWAceevtpoPvP9+Qp7C\nW3iA9Ju+5+AP8hAxOAiFoSI38KhHBGk0+PjHVRdavyJKBIMLH48I3jcb+pnpm6uHFxRzSMjl1Mfg\nkMCZnGaUeZroZIM+tIcG1YLg/NDGXHhlO/DMw99JvP12P5wIBOQLiPEaxefezp0fer1SGF7wAvjz\nP4dCASHg5S+H7/ke36584PCI4BpZWuDEmwi6pMB7wDUaWIQHsiqJINgemQyMj1NmgwJqxWW3lALc\nb0TQsiAVKRg/FCIIauH1R3/EK/7+PQxFxlELNMg98FW+Z/zTALz0perruRykbJNLTPEAyjOaL6v3\n7b7bQkFlDZTLDLNInoZy+GjSH3ci2K5TrrUYvhmVJpI54E0QwtsAmEYt1pyWrforY3x9t3P1SAlG\n09k8F5oQwc2YmoJcjkFW1DMcidNSF89+dQkeeSIIXQTFfM2BDf+GdJZz8KpXHfDRHS6Gh4GBAU5y\ngZJbL9DYwLLgwx/u6aHtCVFraPDG6ObvXI4mhj7zcBWkw0A2q1TfiQnYGD/JGNdoBuYEH3ywt8fX\nLYKuQHfhIAQ0F/ydRIGk9JzD8FmGMTQU6nv1ju3Zz89R/I9vV+WCn/98qIz+0KEtZHlMspjgxHtu\nzL2OPUWw0fA3rACymYQIboXJSVJIVT6MChaw7XgrBh0VQeOQgmI6YODFz+Jl73oGhakRXE2wSJPX\nyE8w9kOvgc9+luuvV7xRCCg4DSSCa4whwEsM3XdbqIvJSa0INgGB1VQ3y7gTwbbW0JXwzag83abg\nOYFHBK/jAqCu77grgu02cy0LhGl4rp4CTTK10X2K+D5iSKXg1CkqrJNHMWmrqW6a+zUnmBDBAM79\n60rIOnjr8FOhUuajgKEhYKBKCng2X1Rf1E/ff/on+PKXe3Zoe0JQETRNf0GZTvtR35cuytDPTH/z\n0fSj12pq/b8+NM0Y12i4iqBODpVy65+PE9olzAGI9XBiqHjWAQfFdMAznuG7DYRQD7sXvrAnh9Ie\nWhHMYeqSccc7/3Emgt4isdnEItCnmkmI4JYIzAkCXrBAHM+1i04zgmnzkKojtsCrv00wcmqQTH2U\nPAajXOON/JFa0b761fDZz/LS9D/CffdRWLjk/VwWk1S5xOnTB5gOPDVFhXWKqK5Fp2UjZfyJoHtt\nBzf5rNVG6HvKJw/LMtFn0ETwFI8D4Fi+TTCuCFYCuZs+rRaIaF1IogZ2GtNi1QAAIABJREFUxqlT\nFGl4RNAx1YuaEMEuECSCwcTQkUD4nGHAo/cHDMzArbcdvWHlSgXS2u9SY5ancb9++qrV4a//+uZF\ndz/AJYK53Ob5QCHAaDgsrvjnM4VD7SW3HfJRHg7GxtT+hTlU85QBizSsrbG85HD+fI8PsAu0jRq3\nwivbQ0kM7YAXvEC9v3I5pWjkcvC85/XkUNpDE4MsLXK0QKcLShlva6h3vptNWgQKhhMiuDUCyaEA\n0nJiv1DsqAg2D6FMfhvccQe86EVw4pYqU2eqvIXf9vu8VlfhRS/iJb/zRlhZpsw6Kf0cLbMOpfLB\nqYEAk5OkcRjVx+NY/VEq304RbEWJ4Omj59bZF2giOMlTAEhb4miHZZBYxwntrMAAIukQ3DlmZkJE\n0DbVi7lfgTHHjgh2UgQffBDsVX9lNM0lhp559HpscjnID+Qhm8OkwFv4iFp1GSbNJnz1q/DXf93r\no+wOjqOtBkItJtrNB159YB6kzybGs0ukJ4/mwyaXU2Rw/FSZRmmcCa6oOUHpwPo699zT6yPcOdoR\nQefafOgL1aLVs9Cfb/omf04wlVKHEakL7C1GRiCbRaDsNyBBKnIQR5WonSLoJd8CZLIJEdwKmviP\noHY8pd1f1lBXJGitNUnZ/sZsKWP25MISAt73PvjlX4YP/+U03/kLm+uGJrnCWc6RwWYKVR8xOJSC\nSpkXv/gAD06T/nE3TVDbgK9di7frox0xMFdboe8p3TB5iEfUR9DPuREWvVL5OFv9oT3xB5KgmG4w\nM0OBJgVXEbSUsydRBLuASwSDMbtCqOAUF+fOEdoiP8s5lQZxxCAEVAcEVKvYpJjhIi/lM1hrGzSb\n6qH8kY/Ed3epHYK2UCHazwfOnrsW+platclRRq2mnhnr46eY5lKoT7CfiOCmmTFAXr4S+p7qRLln\noT9jYyoAJpVSC9kf/MGeHEZnCOGRgwL6wWvbsVUE280ItkJEMJOMkWwFfa5H0aFnfWANbasILqyR\nwr/oi6Ml34N9yMhkVMvQzTcD73kP/NRPbfqel/F3AGQHKxRvuQ7x9Du4+WaVGHpg0ERw2E2J1ed6\nvzvG9hubqgSaTVrB9YYQlE+NHfZh9Qf0G6pIkxwGOHbsK2I6jXck1RFdYGaGNA5lnWLsWoITItgF\nXCK4tOTvlA0OhuPbo0TwVu6H246mdbBaBQYGsEmzSpW38BGsNZNWS92cFxfh0Ud7fZQ7RzQopl11\nxNxD4SumNtpHTHcXGBtT64Tm8KRWBIvKtLS6yte+FroHxxruuQ3ucFuX50LfMzAzdIhHtBnveQ+8\n+c3w9rfDd35nTw+lPTQ5KOlZIjd+Om7kwHHUIjY4S0KzSZOApSMJi9kamhzUUP5At1R+bS2+KpF7\nroMzgq2lCBGsxShF8kMfgne8w//8xAle9K7nkHr+N6ku04lJEKmDtYWCpw6pUnnDO9egVMG4YpMi\nuLQUvsYLBUqVY7E07R76nOcxyEfCv/qJCDoOoUVIoghug5kZAKrovs19JoKZ7b+l/9GpQ9CFlHDu\nqzZs+G/MW7n/kBqfDx8uEXRIc46zXGOUxprFNT2SkUqFZynjDtdOtFV1xNz5dQikD9brR6s2Iops\nFs6cgU/XRkjjUGKDFllyq6tYFnzlK70Ns9wp2hFB+8oc4FuHKjf01uI7MwPvfa8S32LZRuIRQVcR\ndGKpCLrnOpsNdEY2m379CSQzgttBn+shFkljI520NxO6seE7JOKE4ELRXSzK1TVv3g6gOBWjFEkh\nVBHoW96i3qh33MFQOs0LflYFBYN6Fh2oLRQ80j/ACjlMGgFSsLKyxc/1GJsUwaUlDPJ4t85CMZbv\n01hAE8GcmwKte2GljD8RDLrMHFtuJoKJItgZmgj6s9/7Wyp/rIhgkNwEieCVK7D01AZuYEqBJtef\nco5slO3gINoamuYLPJdLTKvOJr3eajTibS2JwiWCbjpoW2voRRNceyRQmwnYzY4oJiZg4tYRrn66\nzDSXmGOc3NoaSId77kn1BRF0CYG3myglrdkFgkRw4JaD9F/tDD1yre0MmhyU3bqYmCqCQWXfvaZl\no4FBwRuST4jgNtDkYJBlsrQw7Yx37aytxY8IuiqwEAFiAIjIm7N0YoTY4dZbQ5/+xE+oBfnsLHzv\n98L4+AH//YFgoOi8WFxJgZS+Euieb7m4hElOKVyAKOYpFjv8guMOTQQFyh6KYwMSxxGxu5+7aDcT\naq83Q1+oZAzv/ZygDfR89LAmgqoPWLK0tD87z0eeCGYyvgU0yJ6DiaFRW+gtPED69vBN/ihhcBBt\nDU2xwiA2GUrmEumyujYdpz8VwXZE0FMEZ8O+qPrpmK2IDgCjozB9c4Un80PUjad4gpMMyFUwTO65\np7D9L4gBNoWHLCyEFoxkMlSvS6LGt4R+wHrdck48S8aDRNDdAGg1bETQIljNhCz9CSIIkP4MLQwn\nj+NIQLC6Gr82pHbzgQCsrxF0cBRPxT/Ya3gY3v/+Q/wLNSlwSb9KBFbnOq6KoHsfT6cDVQILq6QC\nqn+xmomnsyIOGBiAUgk2NiijxAt1P0/Hlvy3UwSthXATeqVeiqmdJiYYGICBAUZWFImRSOymxdJS\ndpsf3BnivI+9L9hJYui5c8BaJCjmiM4Hgvqn5cermOTIYfAMvsLLzU8ihCJLcR82jyJKBNvNCM7O\nhy+Y2pnezpUdBjIZvWldLlNlFZOc0ryNJpcvw6VL2/yCHsMNPkilAkTw2jXMYK9cpaLCjxJ0hiYH\n1YgiGDdr6Kb0SNvGtAQiYBGsDB75vcu9oVqFcpky62SxkKiEOYinStSWCEqJXA2/OUunY8Zg44DB\nQSgUfEUQsFvxPdfg38czmcBmz2J4HrQ8dPTdOruGEN4GQEkHh7j387iS/7ZEcCm8C1meHCTBNpiZ\nYRS3G0bgGMbhWEOFEB8Fth0xl1K+aX8OZ/+xYyIYDYq5/W0Hf3A9wuQkvO77Cjj/z1+T3VimxhyP\n2Ddom0EG294/7/FhYDtFUEqYWy0AvhWhdvagfTvxwM03Q3Egi7lQYIyryoLTNGAQ7r2Xg0212yOC\nCpH3kGs0MIPBAvn8UXVw7x80Eax4g+Z9ogjq6ghvkZjJUKkmpH9bTExQeXRRJUkidPlwOnbnG9qX\nybOxgRNcNaZSFE8kqv8mCAGTkwyf11UCCGx9ruNOClIpnwiaSxsI/AVZaTTxhW6J6Wl45BHf6m/F\ntw4I2tdHWIsRIngiRjPAccXMDKPn5lF6IDiGxcaG2kzL7lEY3E4RfAR4VH8sA9+O8mtc1D/7GiDW\n2pFLBKVsTwSbTZ2Que6/MY9yYiioN006I8iemCCNzQ08So05hL5i+8kaKmWYCEYXuOUyLC86mE1/\nx7HEBuUzMWZA+4ixMZietDHIM8EVGpTAUC/Yvff2+OC2gbtQCFoFaTTCCXOZbEIEt4MmgoMBa2gc\nFUGXCHpqQbNJi2yACCbnekeYmKBI05u5clpqBRbHhWK7DkEWFnACtlCKxSRFshMmJz1FEARWU53r\nuCuCQui5bylxVsLBQOXa0R/b2BO0IljRiqC0LRwnvue8nSLYWt4IfU/l9PHYmN8TZmaoskYOExA4\nTXXz3A/33pZ3Vynlz7sfwBng30spv0dKebeU8nuBfw/cvPfDODi4RHB93X/oFAp4w8gPPojaMdUr\nzWkuMZRe06VBRxPe7sH0NCd5gjwmoyyECnzn53tzbN3CNBX5y+XU3IFphuPI83mYe3AxFDtZyy4i\nBo7HijKdhttuVi/IMEusUkE21Hv9vvvCAQ1xg0sM8vmtUyQTcrANvHRB/cSw400EPTSbOKT8NMEk\nKGZnmJwkj6EXDPG2C7a1hs7PYweXJsVSEh7SCVNTlNnwOkIdrQ7F8VxDmyqBZhNagQs/lUoUwe3g\nzYbqk6xToON6zl3yH7y/myvh/qryDROHeER9ipkZKpoIStDq/yEQwQieB/xr5GtfAGKdPbhdYmjb\nIvmbbw57So8YUimVaDZ4wxgzPAnAMItkHHWlShnvHqIgtpsPFAJm7w//Y+qDR7tMPornPT9FgQZp\nLHK02NhQpLjZhK9/vccHtwXapUjSaNAIpL+SzTAQo4qxWEInhAyhB/Slg5Qydt1yLikIlsmHDi8h\ngjvDhHJ6FFAXTVx7I6EDEVxYwApOrZSKCRHshMlJBDCobd9Sl8rH1RrqkgLvGl9awiFg9y4UKFcS\n+/eW8PojdZWA3R/kP6gIGqvhXb/KmalDPKI+RYAIgsBpqRd0P8a4uiGCXwY+KIQoAuj//wDwlb0f\nxsGhXYfg5sTQ42MLdXH2LDzz31U8S8YQS6RahvffFxfDBaBxxU6qI64+HE6oqo32wT9sH1G7vc7T\n+AYtcoxyjZWGbyi/554eHtg2aJciKRvNMBFMyMH2KBZhcJAKqwgcJBKnZWFZbVS4HsI9lqBaYAct\ngkmZ/M6gyUEJZb+SMVYMgtd4UBEMEcFikVLp0A+tP6DV/mF0mqCukIjjuYY282IrK8ggEcwXknO9\nHQJpsUBs64BAbTRuIoKWhdEMr8EqNx+PUZ09QRPBvFYEHVNdRIetCP4A8AJgWQgxi5oZfCEQ26AY\n2LpMXkq4/342K4LHgAgCcNNN3h8z2FQsvauou37iuqsYxE6qI2YfD/vR65PHa94kdd1JXs1fIXAo\ns86GmfEWYHEmgu1mBK2GiRO4beUK6aMs3u8f9NxYFvU0ltpWEqfFQzsiGJoVS2zAO0OH3sg4kgP3\nus7no4pgIP0gsYZ2hiYFo+hZjn5TBLX920Mum2z2bAd9zr1OuRhf38G6EG+8Y2kpvJmbz1Me7Y86\nq54iogi6lv9DJYJSysellHcBNwLfBtwopbxLSvn43g/j4OAuEoMvlksEr1zRX9fVEQWanOY83H77\n4R5kr3DjjaFPq+Y13JDYfkkO3Ul1xNzF8CBcbeaYMYd6neuzl3gWX2KEeVpkWFpQd+jHHovvPGi7\nGUFzww5FjVcHjxep3zUmJynS8JMkdYBIXOYEHUftGAsRUAsajbAylITF7AxaJeqHVMFNiqCUsLAQ\nrohJFMHO0Od6DDX+EGcbMLSxf0c3e9KZ5FxvBx31PaLJv9sLG0ciGEwFdp/hzvwircD1LYqJCrwj\nnDhBiYZn+XdsB6Q8dGsoAFLKJ4B7gItCiJQQItYrsYxeRwR3yNyZonPnAKS3Gnoa3yCNc3wUwbEx\n3S6vMCwXdIWEulH3Q5fgjhTBufDMQe36Y7blmErBiRO8mr9ikFUGWGZx3vFu0nFND3UXidms/xAx\nGk6YCA4l7eI7wsQEBZpksZGAbMVLEQzOigWDgaxEEeweXl2ITom17VjWhUBY9W80gPV1bLPl2wVT\nadKlnPccTxBBgAgKHKStrKGGEbiOYoRNx2QY4WCgTMYb6UjQAZ4dWC/QHAfHkTSb4Tm8OKCdIti6\nthx6hpeq6aRLficolSiOlMijXkiLNNIwWV7e5ud2gB2TOCHElBDi40KIecACWoGP2CP4EHQXE+fO\noVablvon3Mr9ap7m9OnDP8BeQIiQKjjEIkJfuZbVHxUSO5kRnFuMlMnffAw7a06e5CRPcjtfpc4c\nTsv2lMC42kNdIuhFjQO20QpOlFAd2WOBznHBxESsFcF286BufYSHZB50Z/BSYt1UwXhax6QMq/6N\nBp4t1FsoFouUSiJZKHaCPteqQsICTQQhnsS/nf07rPqnE3VoO5RKMDREiQ3dH6mT74nfOXeJaZAI\nmvOrISJYHkye4TtF9uSk5/SQCKTRPHRF8LcAE3gpsAbcCfwl8KN7P4yDR/Ah6C4m2iaGnj2r3rXH\nBYE5wWEWwVZXrm3HXxGMdgjCZkWw1YL5Nd8KKpCMn60d4lHGBCdPIoC7+Bdu4UGwHZaX1ev3xS/G\nLxjIcdS5C5JApEQa4XST6tgxs/nuFhMTFDA0EQRHzxfEkQgGE2JNcv43ZRNFcEcYH4dUigEdJiGR\nOLYTy0WilMq1k0rp8766SossaTSbKRaS+cCtMDoK2SxDLJPTpMDWmzxxnBNsrwgGiWCiCO4IU1OB\njT28BMm4bfYEFUr3OS6XlkObuZWxZD5wx5iZCdzXBU7DPPSwmLuAH5RSfgWQUsp/A34IeOfeD+Ng\nYVn+IHoqpZQiKeH8ebz5QDg+iaEhBBTBUea9Uvl+mBE0DL9DMKXfydEZwWtXpVegDjDCApnTM4d8\npDHAjPo338bXuYFHKacb3jleXoYLF3p8fBEEiYGXbGlZSCdQKCAElaHEM7YjTE6SwySnw2IcK14l\n40EbsEcEm00MAkQ/mRHcGdJpGB/360IAx2jFbpEYDIrxEmwNA5s0wi0OyWQSIrgVhIDJSSqsknGJ\noBlPUgDtFcFWhAgmiuAOMDFBiQ19PxdegmTczvmm3kgg6mUs15ITvmOcOBG+r/eACNqAy++XhBDj\nwDoQ+9zXqEokhLI9WhYec6iyqrp4jktQjIuAIjjGNU8RdJz4E8GoGgibraHRMvl6dpFjuZo8eRJQ\n6bDP5ouMpZcQAjY24jkP2tYq2GiEE+YyWaoDiWdsR5iYII9BVpeMO5Z6MseFCLozglFraJgIJorg\njqHJgcABBNK0aLXiWRcSUoGbTVLYvmKQEMHtMTlJhTVtExRIM56KYFuXR7MZVv3TiSK4I9Rq5DH9\n+3kr3kTQCwCTchMRrEwlRcA7xswMw6iZLYnANsxDt4Z+AXil/vPfAn8CfAz44t4P42DRzhY6N6e/\noDsEx7mqPj/GiuA4V0OKYNzIQRRRIhgNRKhUYPb+cCRmbSiGE/SHAU0EAZ7DvRSsVYRQizHDiN+i\nodMiMRQskPTK7RwTE+QwybpWIlvGKkCk3fmWjQZNArs8yYzgzlGvU2aDLJaaIYqhdWxTUAxAs6kC\n21wkCtH2mJxkgBV9bYvYKoJtXR6GsWkOOCGCO0C9Ht7Ya8VrY8/FJiK4tqbSLl2kM5RHk52eHWNm\nhiGW9AYfOIaFYQTWSLtEN0Tw+4DP6j//H8A/AF8Hvntvh3DwCN4Q3R1lRQT9xNA6s+o/HDciGFAE\n68zq4BzZF9bQKBE0Tf/Gk82qB87co+GnYX08ZsNwh4UAERxglRn7ce/ztbV4LxqCiqAVsRElxGCH\nmJggjUNBJ45JSyVJxnFG0D3fdtMKlU1ni5mkM3KnqNcp0PRniGIYJrEpKAag2fRtoZAogjvB1BRF\nml6aoEsK4nZPD1qBO6v+SVjMjlCrkccgp69v246nIuieZ8+UtbAQdvUUC5Qriatnx5iZocK6LpUX\nOIY6/3tdq3fTI7gkpVzQf25IKd8vpfyPUsqn9nYIB4+ORLDR9DwKNeZgZMRL4To2GBvz+jTqzKqd\nBtvpK2uou1BoZwGefbwR+pna5DGdKZsJz0XeuPFVhFB351YrfopguzL5zQlzSfnwjjE2Buk0Rdzr\nQSJtO3ZE0JsRlBKjGakKGT6m1+5uoFNilSLop8TGkQhGFUEiRDAhBttgcpI8BnmtDtmW6pWL2z29\nkyIYsoYmiuDO4BFBd2OPWHYJbgoHWlsLu3oKheQZ3g1mZiiz7pXKuxt8e62Q6KY+IiuE+HkhxHkh\nRFMI8Zj+PLf9T/cW7aojrl7Fs4WCtobedhvHLqdaCE8VnERzeh03Hvf6iB1VR1wOF+vUTh7ThKqB\ngVBn5Ihz1euMtO34PUDalcnTaIRtREmK5M6RSkG9TsmLngbHMGNDDDbNCOoFokcEU2mqg7GurI0X\n6nVNBNULK2NsDY0qgiEkiuD2mJwkg02RDQCkHc+C8XaKoGwzB5yc7x2gXieNQ1GXi7tdoXE755ss\ni5tcPclmbleYnvaIoERt+iCdw1MEgV8CXgb8CPB0VG3ES4Bf3NshHDw6zggGtsNrzB0/W6gLPSdY\nYV13EVk4jno4Nxrb/GwPsaMy+avht3j9xmPMHAL20BEWQCdHOk68d4+DiqAZWTQkD5EuMDHhdRAB\nSKMVO0XQmxHUIRLB9MjkXHeBep0SG35vpBU/u2AnRTBoB04UwR1gagrA3+Sx7Njf000TsG1aFiHV\nv1hJewngCbZATVVglQLk33HipfiD/+wOhgOF6kKyiQLcFfJ5ykN5XxEkBYax5zyPbi657wK+TUr5\naSnlg1LKTwOvBV63t0M4eHS0hkaJ4HFLDHWhFUEBVFjzBu3imCbpQsrwDiNsro6QEuaWImXyt4wc\n0hHGEAEiOMwiQiuCcdxJ3FGvXLKb2B1CRFDNF8Rh4SClrwhms/q6bjYxgopgov52B60I5tywmJil\nxELnsJgoEUwUom2gx1kG0CdXO3ridk/fpAgaBq3gNZ7JUCodM0fWblGvA2rzHvDGeeJ0zi1LfaTT\ngbCYZM5/z6hMVDxFsBdEsNMVGvsrt501dG6OUIdgoggqVFkBbTOI85yg2yGYz/sdglFr6Po6NDb8\nr+UxGLj5mM2ABhGYExxlPlQVErfd404zgkZkniQhB11Ax8yDtoaa8SCCQRLobfA0GpjkQ4vEZMHQ\nBSKKoIyZIihl2P7tbfYYRqQiJiGC20ITwSrqJi5jSAqgjctDJ4Z6qn9SHbFzaEWwgjrJUjpIKWN1\nzjuFA4VTYrPJOe8SlakBPQ8scEhD0zhUa+ifAX8lhHiFEOJpQoj/DfiE/nqsEbWGWhYszMsQSxjj\nGpw924OjiwECyaGDrHiKYJyTQ7frEKxUYG5WhqaVa8whZk4c0hHGEBFraCqmRDCoEEVTQ0N1Akl9\nRHeYmGAA/0RLw4oFEWwbItFs4iBCfXIJ6e8CExMUaXozgnbM4uUtHW6RyaiNvKAiGCWCiTV0G4yP\nQzrNoFc07SBtO1akAMLEwDTR9u9saLMnIQU7RKkElQpV3JMscVpWrM55cI0WdPWENnMTa2jXqJwc\nJuvOCPZAEfwp4O+A3wC+BPxfqAqJd+/tEA4Wtq1Ks0HlopTLcO0ayKbhZdoOs0h2bAiGh3t4pD1E\nQBEcYlErgjLWimA7Ihi1hs4+uATSnz+oZxe9hNRjiQARHGQFYesgic0drz1Fq6WOKZtVi0RvzqAR\n7pwSiVrQHSYmvIWDSpK0aDQC0d49QqfOyDAhyCZEsBuMjpJPWXqWBBwkOHZsiGDU1t9ooHakTAOb\ntP+N6eQa3xapFExMeERQInCarVht7oF/nWcyeqNPp0D7c8DphBR0g1qNAY/8x8fq72JndSHJZm63\nqF435imCigg290wEt8zjFkK8JPKlf9QfAj/j+YXA3+/tMA4OUZUoldK20KafglJnFq6//vAPLi4Y\nH1cEaWVFhYggwXaw7XRfEcHouX7owfDBjw+HE0SPHQJEsMoqKU0EIV6zoEFiAP65NjesULBAZUAc\nu5DfPWFigiL3kkbPhpoWUqqNsl4uwDp1RjpBQpCov90hlSI3Pkhu1iWCaaTRYnU1vc0PHg6i13ij\ngXfyvRmidBqESBTBnWByksFL/k3cMVusrxewbfUy9hq2rVTgVCoQHGIYyIjqn5zrLlCvM/iYS/7V\n/TyOimA0+dug7n9TMuffNUrXT5DXabEOaTDWD5YIAr/b4esuCXQJYWxZVLvE0KtXUR2CGuNcPd5E\nUAilCt53H6MeEbRxnHSsCEIQrpWoExEsl2H2kaRMPoTAjGCRBhlLdxBJddM2TX9h1ksE5wODn5sN\nO0wEB2OwwuknTE5SwCBDC4tsqFuul0QwOCMYVAStIBFMrKFdIzVRozSrUwUBaZisrsajPqctEdQn\n31P9M+r/E0VwB6jXKeuZUEkKR9eFrK2FWoN6hk72bxmxASeKYBeo1QJ2YNUp12gowp2JQeWq+9wO\nWUObTZoUSCd24F0jf/00JR4FVFiMbBzwjKCU8nSHj+v1x2kpZawZVMfE0IAiWGPueBNBgFOnADUr\nKQBsG8vqH2uolG1mBJ8Il9jUpsMJoscO09NeT6YASs6K5wuMU7hAMEQCAkQwUjBeGTrm57NbTExQ\n0CXjAFLPAve6QqJTVYgVChVIiGDXmJigjH9TjEs4EHSwhuqbupcMrFeziUq0A9RqlNgg66bEmvHq\njWxrE2wTDJSoQ12gXqeku0IlhMh/HNBOEbQbYet3Kp/x7gEJdgYxc8Kb9Xf7gJeW9jbiceQbWzom\nhjYSIhiCJoKjzOMqgv0UFmMYfkRxLqc+Zp8KK4D164751nI263VOAapKwPETYuOyaIiqBR4RNKQ/\nTwJURmIgX/YT6nUKNMm4i8WWDcieLxw6zQhG0+USItgl6vVASqyaIYrjNe46EjYrgmrBmCiCO4BX\nF6J7I814pcR2UgSj86AJEewCtZpy9uiNPcdUC6C4nPOgImgYgJS0jMhm7mAmGe/oFlNTVFkjjY1E\nYFsOlml7WSi7wZEngh3L5APW0BpzcPr04R5Y3KCJYI05TxGMa1iM46gbixD+jnLUFgowdy18h6nd\nkKwkg3OCFdY89hyn5NDoItEwgFYL00n7DxEhqI4kimBXqFQoFFJaERSqr83qfXJopxlBM6IIJovE\nLlGvU425IuiSfymBZpM8Bg20BKgVwYQI7gBaEcy4/WKWuq/H5Z7eSRGM2r+Ta7wLaCKY9Spi4kME\noz3P7kaPGeyNTKcpV488Bdl/ZDKUqulAEJhK1NvLWv3In4V21tCrVwlZQ4/9jCB4BKHOLHFXBIOL\niHYdgpWK4jfXlsOK0fjTxg7pCGOMwJxgldUQEYzDAwQ6dwhGgwUq1WQrsVvkxyp6jggcBBhmz8lB\npxlBM5gulxTKd496PVAXIpB619grd+4h2nYINpsUadBAM7+ECO4cujfSrQtx53/jck/vZP8OKYJJ\namh30Cqwbw2Nzzk3TUUG3c1cy0K7PIIpsQnx3y1Kw3ldKi/2pVT+yBPBdtbQ2UuWv/oAaukFOHGM\n++WgrSJo22pHMQ4LhyDchUNwgRCtjpifB6dpel8bYon89dOHdIQxRkARHIgQwbjsHgcXiUGFyI7U\nCSQPke5RGKv4ViJSYJqxnRFsRmLGEyLYJSYmPOuYmhtTz7xen2+17K4uAAAgAElEQVQIn3NvSsMw\nyBOIl0/CYnYOrQjmsNTi0FKqS1zu6Zs6BKGN/TsJDukKEUXQPee93tiDzomhQGAzN3mG7xalkWJY\nEdxjqfyRJ4JRRdAwYOWqbwtN4TB6qhKPmKVeQhOECuvkMMCxcRy1cxOnjjnYfZn8sSf7sJkIOooI\n2nY8dhKhMzGIBgskxKB7lOoDZFwrEQJMo6fEQEp/Ty44I2g1zND5zhSSUIGuUa+Tx/AXijFSDILE\nIFgm725SAJBJUyiQzBDtBPU6ZdbJ6MWhtOI7Ixi2hib2711jkyIYn3PeKTFUEriYE+K/a5TrZXKo\n+hWbNBhN5ud3//uOFRGsVNzqCN8WOsY1Ujcc8/lAgLExKBYp0KRIE5DYloOU8bOHbkcEy2WYfXgl\nXCafW4pHjnavESqVX0bE0BraaWbMTnrl9ozCxJCfLKgVwV7uILdaigxms2rB71eFJKECe0a9TpGm\nt1CUMUkVdM95JqOs/UEiGDznSa9cF6jVKHvWUBUgAfFUBMPl4oHxjXRCDLpCrabJv9pJk3Z8iGAn\nRTBUF5LY/XeN8kSVIurG6VpDr1zZ/e878kTQ3e0WQitF7YJijvt8IKgX6NQpMtiUUC+abNl9SQQr\nFZiLlMnXhlskIDQjOMAy2GpxGBdFsNVSpDSdVh/B3UQ7WHua7B7vCsXpEU91UYpgb62hwflA0Ofb\nsjYHAw0nnZFdo16nEEwVjEmYRLQnNEgEg6nACRHsAmNj5DHJu3YxCUin56TfRVtrqGH4VSGQ3NO7\nxfAwxVSLnA7/sqUEx+759Q1tEkMh6YbdR5SnBinS8GcEmwkR3BHKZbX72LZD8LgnhrrQalEVdSeR\nloVtE7tS+XZEMDojOPto+AlYrx/CgfUDAorgCItaEZRIGY/d404dgjQam+oEkkVD9yhMjXjWUDsG\nimDbqpBGAyOYLpfJUE2CgbrH6ChFYfrWUFstFHtNDqLX+FZEMJkP3CHSaQpjFY8I2qSRRisW93TL\nCm/uBYlBKBAqk07u6d0glaIwXiUbnBWLSTJwUBH0NnMbDazQZm6WgYFDP7QjgcrJ0YAiqKyhCRHc\nAbasjkgUQQUdGDOIHgq0nFhWSOzEGjr3pBH6mWNfJu9idNRLXxhkCXDAcWKjCHbqEFSLhsA5TGwl\nu0JhZlx3jaFsOj1ODY2ebz9mPB8ggkmH4K6QTlMcygVSYuOxUIye8zARDCAhgl0hMzFGEVUmJgFp\nmrG4pwfVQPDPv2waYWtoogB3jfzEcDg0JGbnPKoIhlw9yXjHrlE5Pe4pgnZiDd05kuqIHUATwSGU\nBCid+HUJtusQhHZhMZEy+dPJEwZQL5xWBUdYUF+zndikhnYkgo0GrcRGtGdkT9QDO8gCafTWGtr2\nfOu+qSRmfO8ojpQCvZEpaPV+oRglBkEiKIPfmBDB7lCrqW5YDccw43tPt21aNgEFWFCspEknDvCu\nkKmNUEDtjDukYkP+O80IRol/ogjuDtXrRinQIoVUGwCWxfKCHYw/6QrHjgjOXpEBrTpRBEOIEgQr\nfkQwuIhwwyOk3GwNnZsPP1FqZ4YO6Qj7AHpOcIQF9SDWhD8OD5Do/FDHYIHEGroriKlJSp5qIJA9\nVojazgg2GuHi4YQI7hrFsbKfKqhnQuOmCIa7I8MLxSTfqwvU6/5YBwLHaMXqnh6y+xsGrdA1nqZc\nTuzf3UJMqLRYF3E457at7MCpVDgJevP1nTg9dovKQApRLlEgkKZuNJmd3d3vOzZE0LOGXmgoWUmj\nVm3C8HCPjipm0IrgKCqHVtpO7Erl3R2PoC202fS7DvN5tbhcXfMfKhkshs+MH+JRxhya8LvnGduO\nnSLoLhrch4hsNDAInPTEVrI7jI9Txt82dGyb9VVnix84WARJgWX5imCLrL9ITGzAu0axVvFLxmNi\nHesYFmMYkZmxhAh2hVpNBYBpOKYKDpFyi585BESJv2mSqP77hYAKrMi/1fONnqAaCFvM+SfP8F2j\nWgXKZU0EtehhGDz11O5+37Ehgp419Mlm6Ou100lesQdNEOrMqc81QYhTWMxO5gOvXiXUITjOVcRM\n0iHoIUAEBXjneX3dJ9S9QidrqN0wlaKhkcmnve9J0AXSaSpFn/g5pFhbMLb4gYNF8Hx717G+yP3i\n4YQI7halCb83Mi5EsG1YjFROnWZCBHePep0K66RxlNrfamHb7Noutl9oqwhqIuht9iTVEbtDreap\nwABOq/eKYHA+MPg5zSYNAl7vxBq6a5RKaEWwgU1Kbac0dx8Yc6yI4Po6bCyZ3teytBi8MVGKPExP\nQyql5iaRIB3slh0rRdC1gAaHyqPzgQvz4TL5UeZDtQnHHpoIFjDUoLkmglL2vmOsExE0N+xwr9xA\nOumV2yVK1RQg1WIRgbne8iyah422RLDRiMyKJRai3aI0Nez1Rso4h8WYJiD9hWIqBSKVEMFuUKtR\noOnPAJvxqAtpqwgaBmZQ9U8Uwd2hXqeKb+WRpkWjodwVvUKU+AdTQ5tBV09yX981UinIDxZ0YAx7\nrpA4NkTQ6xCMVEeIG5L5QA/ZLExPKwVNf8lqWiwu9t5e4sK1LwZ3kqLzgQuPr4Tsv6PZVZKtpwA0\nKRZAgQboIto4zAl2mhE0mxEiOJQhwe6QH8jrABHfLtirwJjgjGBQEQwVDyeLxF2jNDXk10fEoC4E\nOlhD9WpxA73Dl1EWsoQIdoF6nSINnRIrPCLYa8v/Voqgbw1NqiN2hVqNIk09B+yf815e41HXlnv+\nnUa4N1JkExV4LyiNFiNdgo2ECG6HajWpjtgRTp5kmCXSqBuKYyi1YGOjx8eFIiprayokJriTFLWG\nzj8SljBHhx0S+SiAQJdgmQY4NlLGgwh2mhE0mzJEBKsjSR3IbpEfKIRL5XtUISFlZ2toUjy8PyjM\njHszgrYOi+nlNd5qqfOeyahOuVZLqxfNJmlsVtEnOqM2ehIi2AVqNYo0vHoYx4qfIuhd85vCYhJS\nsCvU6xRoevdzR0uBvSSCbRVBKTENJ/QMLw9mSB0bBrL/KI5XAl2CqcQauhNUKkl1xI5w6hRFGuR1\nJLGtd5jiYA91B99LJUIx01Fr6PyF8JNvZPzYvM13hoBNtsS6Vk97Xypv2+ojlfLWgeqhYlmYTioU\nNV4ZTojgbpEfLvo7yD1UBC3LJwWpVGDB2mhgJZ2R+4LMdN27l6swid4qgh2DYppNijRYQTO/jLrB\nJ0SwC9TrlNjwFeBWvBTBXM7fCFCdcqnQHHBCBHcBzw7sKoKKaPWS/LdVBA0jPBOaSlMdTNZle0Fp\nohroEkwnRHA7lMuKOCSK4A5w8iRFGhRRd2/HVDtMcQiMaWcLhc3W0PmL4UCg0YkkVSSEYhHG1Wxs\nhVVAenOCvXyARGdJQD9EonUC2QyVaqLw7haFkXJYEeyRXTBaHRFUBM0gEUwUwV1DTNQD3XIC2bJ6\nmiTZNigGPCK47BHBRBHsGloRzLh1IZba4Ot1PUxQAfZG9w3Dj72HxP69WwSIIIC0bZCyp8/xtlZg\n3SHobeYmm3t7RmlykAIGwu0SNAxWlp1dufeOBRF0bzCzF1tg+iEiNXEtZJNLQEARVE9oq6UW33FQ\nBDsRwU1hMXPhSemRE0mZ/CZoVdCLno5BhURULZBu5WeziUk+ZCNKHiK7R36sGkmSNHqiCEaJf5gI\nJp2R+4LRUUo0dZIkOLaDYzk9S5LcqkMwj8Ea+kRrIphc512gWKRURFtDhUpZblmxuKdvqhJoBmLv\nIUkN3S1yOUolEZgDFtDD5FDvmU3EGtpsYkSe4Ulsw95QHsohCkVKNAJdgsauugSPBRH0qiMeC18d\ntRokGfQRaCJY0HYix1LzY/1CBMtlmJ8Pb3ePXpesJjbhhKrTGHCjpy0ndoqgYej5oYa60fnBAgkx\n2Avy41Wy2IEkyd4ogp2IoN0wlNVFI5XLUCySYDdIp8mX0pr461mSVu/soVFiEOwQdJNsAS9RMDgC\nkGB7lEZL2ibon+s43dPdzzEMLAKBX4kiuGsUhou+Ctzj69tVgLNZde16M6FRV0/yDN8zSiWgUqHI\nhr+pskt76LEignMXwtug49clW1CbcPIkWSzKKH3ZaanZsV5bQw1DfWQy4eoICB/b4CDML4UTJUdv\nGjmEI+wzTE8DMKgLiKXTe0UwahvzFjDNprvXqZAsGvaEwvhAgBiIns0IRheJ7vk2m9ILqwKoDiVV\nIXtBoZL1Zoh6aQWGDtURoBWioFUwndhCd4HSaNGzCbrzv3FVBO1QIFSSGrpbFEZKkRTo3imC0flA\n0/RnQh3S/jM8sYbuGZWK+p8SG/69MyGCnVGpqDfj1Svhtuz6meRJswnaKusSBBwbx5EsLPTwmPAJ\nSrUaDgBtNv3FRCajdqLWN/xvSGMzeFPtEI+0T6AVQe88Ww5SxksRDIaHBOrF1YxgsmjYNQqTQ3pG\nMFApsHr4Q2PBGUEp9ayv42CaESI4klSF7AX5wVxYEexhcuhWYTF2JCk2IYLdo1SveDZB2WNSAFso\ngs0mrYgimFhDd4eiVoG9c95DFXirDkE7MhOaWEP3hnIZrQg2/de2kRDBjqhWYXkZzDW/TL7EBuWb\nT/TwqGKKahWGhxlCyWwSsBtmzxXBTrbQ5WX/z4OD2sIaKJMfZhFxYvrgD7DfoBXBYbTn17Gx7XjN\nCAYVQRkkgkkR7Z5QGC6REY62hgpwHNauNbf9uf1G8Hw3Giox1l05pFwbcDpDpXosHlMHhuJgQSsG\nve8S3CosxooEBCVEsHuU69WwNTSuiqBh0Iqc72Rzb3co1YLnvLcVMZ06BN3xDg+JNXTPcBXBQnBG\nMFEEO6NaTaojusKpU4ww733qNI2ezwh2IoJBgjo0BAtPGWC1vK+NigWo1w/hCPsMWhFUhF+C3fsZ\nQffvdh8QQUUwupuYPER2j0JRkM2r19N9gKzPHu6JdxyY17eYgYFwUEyaQNhTEgy0ZxSGCxFy0DuV\naCtraJQYJESwe5SmhrwgKDuGM4JBa2grGAiVhMXsGkoFDm709O763soKHK0EShTBvSGoCCZEcAeo\nVNzqCJ8IJtURW+DUKUY1EZQInKbZUyIYtCxuRwTnH5oP/ffRspEkDrSDJoLDLCqtrceKoGXBxobq\nk3MXBEFF0A7aiBJr6J5QKEAmn8azEgFrc4cbI7mwoKyh5bK6PweJoN8XSXKu9wGF4ZJHDmSMraHh\npNiECO4Gxalh8m71Eymk0VtraJQYBMNijMj5Tq7z3aE4MUQGM6QCx0URDFpDo1bgZINvb6hWgUqF\nPAFbfdyJoBAiL4T4ghDiy0KIrwkh/pP++rAQ4tNCiAeFEH8rhBgM/MxPCyEeFkJ8QwjxLYGv3ymE\n+KoQ4iEhxH/b7u+uVmH2SiDXFk0ET5/e53/lEcHJk4xxzfvUabZ6ag1dW1MKQrHod4652EQEHw0f\n6MiwcwhH2IcIWUMlOA5S9q5zKqgGplLhr6nEscRGtF8oFiFbyPhWImB9/nCtoW7EtSvWB891iAgm\nC4Y9ozCuFIOgdaxXvZHBTjkIE8Emef+bEyK4K2SmapR04rcEZKu31tBOiqDTNEObe6lcxiMPCbpD\naXrIUwTjNiMYVoCDz/BkvGOvGBoCymVytJDuMMUuuwQPjQhKKQ3gxVLKZwLPAL5VCPFc4D3A30kp\nbwb+HvhpACHErcDrgKcB3wr8phBeTMh/B35ISnkGOCOEeMVWf3elAlfPr+khFIVabtkr1U4QwalT\nyjqLUgRtvatoWdv83AGhkxoIYSI4PAzzF8IrnJGxRA1si0oFBgcZwU0BktiGzcpKb8qmg2FALoKK\nYNIrt38oFCBTVA9lTxFcNLf6kX2FZfm2UJcIBhVBGbEBJwuGvaEwXt0UFhOHuhCIEsEAE0iI4O5Q\nq1FmFaGrOBzTwjACStwhwqsOIEIEbRvTFgi3SgBBeSCVJAPvEqWZMbKoF9rucSrwVjOCoWd4khq6\nZ1SrkCtlkPkSRTa0Kih31SV4qNZQKaXLU/NABrVp9RrgD/TX/wD4dv3nbwP+WEppSSkfBx4GniuE\nmACqUsp79ff9z8DPbEKxqHYg5x4OK0XjU1mSO08HnDzJICvkMAGBYygG2CtVsNN8ILSZEbwcVjZG\nJ5OeyI6YnqbCuk6QBMe0cBx6Ujbdjuy7X5ONBkZQLUjsgntCsQiZUg6vgBhYXz68XZ6rV5XCPzzs\n7xwHiaATCQZKzvXeUJwYJBcDxSCqFkDApGMYNAiURSZEcHeo1ynS9CskTLX53YvzHeyUc10epom3\nsed3yqWpVJK12G6Rmx7Xa7Xe2oEdR51zIXznlnd960J5D8kG356Ryaj1kqxUKbPm93Luwh56qERQ\nCJESQnwZuAL/P3tvHiNJep53/iIzMiPvrMq6rz5mpntucjgcciTxFCnJ5HogWcJK9sq2tN5/LNkA\nAWFXgCFhDQmyIS0WgoU1YBsGFl5Yi8VqpdWaMiXxlEYih+ScHHJmumf67q77vvOOiP3ji8iIL+8j\nMovT/f2AxlRWRVbXdFREfM/3vu/z8DVHzM3Ytr0JYNv2BuB6/S8Ay763rzqfWwBWfJ9fcT7XlFqG\n4B15dTt9XiUUt+T8eZLka7MGpiMEz2pOsBchuLspL2gnltR5bsniIgYl7yFSEf92Z9FKVF8R9M+F\nVgtVb9EAIiBbJQr0TTwOkWTUmykBTka4cHAfUn4Pp5oQLBTkoGm1czww8dmsb4bo7CoGLSuCthjb\nyOMLiFVCsD+mp4k5QtCtCMLZCMFmwr9UwnEM9QtBZRQzCKG5GZJ4QbB2pUKhYI+8g8tfDXRrLP6K\noCwEVWtoEIyPA6kUSU691ts+hOBIl1O2bVvAhzRNywD/n6ZpTwL1jWiBNqZ97Wu/xdYW/NX3tonw\nCOM8A8DMJWVZ1JLz54lTwKDEMWnMsrhhn4UQrFQ8E5H6ykB9hmAy6bWcuUxcUOe5JQsLGHwHgzKn\ngFkV5/n4GGZnR/djuK1Lui5ECvjiBACtmPfiBBAB44r+icVATxm4FSIbOM2L9fiwmySKRRH5EgrJ\nnfn+NuD6GAG1YBiM+OwYEUxqroLVKscHJjDa66heGFQqzuKxWiVkVTjBucFrGoRCSgj2w9gYsVCF\niFWhTATbtsE0OT4e/T2zmfD3KoIRbxZYCcHBSKeJhapELEf82xAyTU5OdDFHNiJaCn+gWqhIOaFh\nQ82EBoErBBOssoPzQPUJwRdffJEXX3yx4/c5k31127aPNE17EfgcsKlp2oxt25tO2+eWc9gqsOR7\n26LzuVafb8ov//Jv8dM/Dd/8j1ex8GTy1BNqPrAlU1PEoxaxstjiMQGqVXZ3R//r4i4Q64PkQc4Q\nHBsTX9891MEXRp17JDf8H/L9yuIiMYq1iqBdEf9uo64INjvH/h3sSFHezk6N1zkGKXoiGgU9HSNM\nGQvHGbhUpVj0hPiwcGcXpqZkM19/a2i9MZASgoMRT4WJREPYZa8CfLxdBEa7+q4XBn7xn+aYIxzl\np0cATQnBftA04imdyJEjCmqtwKPvjGkpDOpbQ8Oq1X8gNA0jqaMfVzEJ1SIkRi0E6+cDa5+zbcol\nS+rqSY/rajIrACYmcIRgXmoNXV8XH37605/m05/+dO343/7t3276fUbpGjrpOoJqmhYHfhK4CvwZ\n8N87h/0y8EXn4z8D/oGmaVFN0y4CjwCvOO2jh5qmfdQxj/kl33saSKWEVblV8GbHMhxhXD4f6P/f\nfUUoRHwhRwIx0mkShlJRRHCMmHZtof4K5diYqCAd5mWxmntsGkULFhaIUCHq2o1XLaklc1Q0O8e1\nn6FaRa/65z41UmNKCA6KkYkTduaIbDQol0bSLljvFuoiC0FlKx8kiQToMXFfrJkD7ZXavWUouOfY\n3WyobTgViyQ59VpDdbFIVOe9P+LZqDcjeIah8s0qgm5raFm1hgZKLKV77cBnFCHRrhVYOt+hMOns\nA5FcN3RyOWpCcJDW0FGejTngrzVNexN4GfiKbdt/AfwvwE9qmvYe8Fng9wBs274C/D/AFeAvgH9m\n2zU/w38O/O/ANeC6bdtfbvWX1sLkfUJQhcl3Jn5uioTTd24R7jufZFB6mQ882LexS549WpZD9PMt\nx0cVi4toUJsvsJ1Q+bOsCNZ/TuTK+SJAdJ10Rm0lDko0G3csx53FYqXC6UFlqH/n0ZFo845GnZYW\nh1qLIBAuntbNiqlZkkFJJCAS16m1hgInB6MdIDJN75p2K33+a9x1NQVAD5PNKi+3fvGE4NkGjDcT\nBn6zGK81NKxE/4DE0pGa+LfRzsQQyo0s8FcESyVq84E1IajC5APDE4IFqkTEFfXDPCNo2/ZbwLNN\nPr8H/ESL9/wu8LtNPv868HQ3f286DdtrFSjVCcELF7r7wR9Q4hdmSP2NEAhVRwj2akk7KLbdo1HM\nzX0p+yCnH6NWkW1wsgSTTuUX08SyRlsRbHWOW+bKKcfQQIjGw+ghm4qliYUDcLqyD5eGV0F37x/T\n0/Ii31+JTJb3OcH3MyizmIFJJCCcMAC75shaOjWpVBpzWYfF0ZFwFUylqBk9+SuC9dmRqi20f+K5\nuFMdOtuKoHsPT/j2dVqZxah7+mDEMkad+B+tEDRNz5/B345aLNLEJVad76DI5SCaSxKmCtiYhNFL\nJY6PLPL5kHTtteO+r8+mUrDznuwgMpUuDX8Y5n1O5OIiGcTTw0bDKpRGXhEsFETmmGHIu4ouDUKw\n7jxPZM4gPOn9xOIiACmclbhpSsJsFLimMIYhtxD5qwUS6iESCLEY6LoQBbUq0b29dm8ZCNum1lpe\nb0TkF4KJ4p6UJ6dF9K4fZormxOOgxWNOqLxjuzRi51D3Xu1fJMpC0IcSggMRn4jLouAMqkOmCaen\nYsPH3cixLP+MYES1hgaIMRZzgsWpVYFHGfe1syPOeSYjL62bVgT1iKoIBkQyCZlchGosS8p1DrVt\nKJV7Wq/f90IwnYbtG4fS56amVc9JJ7Tz55hgx32FVSyztSVu5qOiXTUQmgjBW/J5zo2jaMfEBBgG\nacQqwcbGqlRHumhodY79FUFb5coFTjTqVoO8iuDxyvBWDnt7ogU0mWyc/fILEj0v70KksmHVIjgg\nug6RlIFOtWYONOqKgXuv9rcE+4Vg/TWuhGD/JKbTdRXB0beGnpyI9Wgy6ZlCnZ466wenZ7R2xtXm\n3sDEJ5LerL8j/kfp8t5q9tsV/iV/K7Dq8giMZFJonHJqnNQAWYIPhhC8J2cITs4ps4mOnD/vhMo7\nD5RiGdMUOz+jwr2RdSsE9+7JW9wTU/f9r/dgaBosLJDBFdBC8I9y0dBsPtD/eYpFyXZaPUSCIRqF\naAwpS3Dr9mn7Nw1Aq4UC+ISgbROuE4Jp5RAbCEamzkCkMrqKoGkK0adpSAKvdo2XSmL8wEVVBAci\nPp1qONejbg1ttsHXshU4rCqCgxKfSBCljO1EAlEusze8Bg+Jclms1TRNtP27WJYT71UoUJYqguoZ\nHhSJhLinVhNZkpz0bRhz36+Udd2ZEfQxdU61hXbk3DmSnBKnAGi1UPlRzQkWi6KVTNNgcrL5192u\nwVqG4KrcRjgxF218o0JmcZGsIwRtwCpVRrpo6KYiKAWMq93jQDAMSCTF7d8Vgut3huMkaVne/Mh0\nkxHE2rmuVNBsL/qFUEhlRgaEkTEcQ5bRzxAdHYnqkH8+0P084GRH+q/xsBKCA5Ccy565g2RbIVgq\n1dxrAXVPD4DYVAqjVhHUoFwZmRDc2hLX98SEPHN8cuJUgJ2FWi0LWBmABYamOZurqRQxfBm8Sgg2\nsr0tv556WDUod2RpiQQFYhSEQKhUwbZGNid47564uUxPNx/nrK8GahrsbpnSMRNLarioIwsLjOP1\nkFil0bURWZZXDfIvBPyfp1j0drlAtYYGRCwGiYzu7CCLJq3V1eH0YB4eiqpQMknTEOHauS4UGmbF\n1IIhGIzxhDMjOPrW0GbzgfXXeAnfELiqCA5EYmHM5yApWkN/2CqCZp0QVBXBwYhPZ5zWUG3kraHu\nmrC+26P299cbvqn7eqDMzgIpsRFQUa2hzbFt2DmQd5WnHps4o5/mfUQ0SiobrlUELUJQLI2kIlgq\neTeX8y3iHuuFIMDeni0dk7uoVhMdWVxkHPcfU8MeYUXQP0firxTk894saqJ6RN4ffK12jwMhFoPE\nmFh81yqCO8Npw3SrgRMtbrt+QWCpedChYIwn5Bmicrl2XoZNMyFYqxYAyeoBJ/hWhkoIDkRiMXem\nDpKlkvij6/Imrl8IWlIrsIqPGJTY3BgGZckpdhRC8PRUXMu63nh/r63RikWklZka7wiUhQVqQrDf\nCIn7XggeHUGl4FWKEuRJPDJ/hj/R+4fUTJK4WxF0QuVHURG8d08sEqanaekY2EwI7h7KC9ncw8ot\npiMLCzVTILfyO6pFQ8f5QCBd2ePYv0hU8RGBYBjCZh6oVQQ3j+NUhxAv15sQVC1jwyCWSxKn5LUL\nVqtsrw8/S7DjfCCQruxziO+LyixmIJLnJmWzmGqF0xNrZEZv/mqg3+jJPxNaUe3+gZKYyWAgXNJt\n5/re37X8aVpDwR8JFKpTE7U1WqHQcF9XQjA4JiZEhESEChaa8FRQQlBme5vGDMGlpbP7gd5HpOYz\n3owgoZFkCZZKsL4uPm5VDYRGIWjbsJeXMyYmHh9eJtp9gzMjKELbNaxSlXLZCwMeJh3nA4F0eZcT\nfKsEtWgIBMOAcCZJjFLtIW2XymytBBu5ks8Lw4BIpLXpU7t5ULVgCIZYIoQREUrAPd/bd/ND/3s7\nzgcCmfJOnRBUM4KDkMzqaFEDHRPTmcyyyxVOh+cFJdHqvu5+3i4WqeCb31dmMQMTT+uEjQg6Vq2r\nolqoDNUQyrbbm4DVKpL1hm9qRjBQkknIzKWcLT5LjNKUihwf2eS7vMXf/0LwnhNG5zAZ2mv+W6to\nILU47lQE3dbQ4VcEl5dFNXBqirYPh3oheLxdpFrxtr/iFJjfYi0AACAASURBVIlfUOe5IwsLxCkR\ndWZKzIq4VkZRFeyqIljaqROCql0wCGIxIBwmES5Lxg1rb24F+ve41cBcjqYxELaNt0CtnwdVLUSB\nEYuJPzVXQWB7pdjhXYPjLgb9baFQJwSLW3VCULWGDkIkAuF4tFYVtB3zkFG1/HcSgmZRhF+7ROK6\nZDKi6J1EAojFiPg29qgM1zn04EBsGMdiNL1e/RXBsl/4q/t6oMRiMD4TxY4mSJAXm6m2DeXus7/v\neyHYECafLTfWsBVNSV2cciqCiIpgQVQEh9VuUC7D2pr4uF01EJpkCF6VF7AT8by8Ba1ozuIihk8I\nWhXRRj3sRUO1KqpFoVCj4PcLwWR+mzxef7CmWkMDwTVtScQsaS5v/e1gB8c6tYX650Hj5knDPKha\nMARDLAaxuOsSK8739rrZ7i2B0Gw+EOqEYH5DCcGAiSdDcoTEiOYEbbv5Bp9tO+e8WqVshdBxNuc1\njVRWrccGJR4HYjEMyp4QLFeGOifYrhoI3rVvFxrNoNR9PVimp4FUigT5viIk7vsrsCFMvkkUgaI5\n8YfmiFJGpyJmBItFKhWGtsvkVgMnJxtDp+tpEILX5AVsLl1B0QWzsxhahYhrJGEBljn0RYP7/VOp\nxn0Z/yJRP5GfZImMChgPgpoQTCBXBK8H1z9WrQrHUE2Tg8T9+FuXUuU9ufobUdXfoIjHwUiEqRmI\nALs7NuYQtaBpiuu8fj4Q5M0e42RXWijqMb2pU7Sie4xUhIiTKzfKLEHXBCgel6MECm5jljNzUIsS\nCOskk+qGPiiJBBCPO1mCrhAsDU0Imqbnxj872/h1y/Ke49Vi1csQBIykqgAHzaAREve/EKybg1Bh\n8t0Tf3ieEBZxiiLwtyiqg8OYE+ylGtgsQ3Dvtiz4J3JDnpK+X9B1YtMZos6guUUISsNfNLRqHwJ5\nkRg6ls9rakxVeYOgJgQzulQRXLsT3Izg/r6oBGQytHzw+891Kr8lGwNFDbVzHBCxGITTwjm0NhNa\nLA61dezwUJz/dLqxOaN2f6lW0cr+FlWNbE5t9gxKPBMh4lTd3IrgKIRgp7ZQisVa9wmgZr4DIpEA\nUikiTkXQBsgXhiYEd3aEGMxkmsd7HR46m8q2TaR47Al/IJNTz/CgmZ+naYSEu6buxH0vBHfWVZh8\nvyQuLxLGIk4BE13s5g0pS3BlRdxYJiYaZ8bqaZoheE+uZExMqyDqbjHmchjujCAhKJeGOmQO3sKg\n2bmuiQPTRCsVpK+lx9VGThB4QjAiObq5Rk1B0KktFOoqgsfrshA0okoIBkQ8DiSTxCl6FYNisSFj\nN0hatYVCfbi4PzIkTDarVOCgxLKGUx3SajOCo4gT6CgESyV0SQiq6IggiEYhPJZyWkOdudBCfmgb\nPVvOJE6ntlBKJaL4NnpCIdJZtTYLmoUFIJnEcCqCboTEykp377/vheD2tlwZmnpIrSy6JT0VIxw3\niFGgSlhUDoaUJbgjEgy6MnRtmiG4LttcTsxFUXSHsTglZ4yVSkPdPS6VRHtxs5Yx8AnBYrFukaiT\nSqtFYhC4sSzxXEz6N17dNQKZAbZtr4W8WyGYPlyRW0OjSggGRTyO0zpU8CrAZyQEa/Nizs8gh4ur\n6IggiI3F5Hv6iALG3Xt3SyfoYhEdXz+yCpMPDGMyLXf25AtDEYKm6ZlATU01P8afIRiRhL9yDB0G\nk5MQzSUwKGO6ERIFJQRrbO/LZejJy7kz+knef4RCMJbTahESVfShOYe6rZ7d7A42zRDckjOxckvq\n6dItxrmZ2qLBdoTgMGcE790Ti8GpKa8y5WKanoukVqzPH1IzY0Hh/rtHxpJS207+lECqwcfHot07\nFmvv/itVBPfuNbSGqvMdDJ4Q9LkKFou1DbigaTcfWCx6Rt5Rs0DBZwaljGKCIT6RwPDnRpaH6yAJ\nUKl4BmD1161f+Gu+eTHVGhocxnTWeY47VeBCgf294Edk3LbPdFpUIpvhdwwN+4W/cgwdCpEI5BYS\nNZd/N0JifZ2usoHveyGYz3u73TpVso/NneFP8/5jcjpc++USQrAQeEWwUhE3Fl3vzujTLwRdE4rd\nXblSlLuoVhPdElmaJea0b5iEsIvDqwiWy+1zIv3CIMmp7CKpHiKBURPgqTRJfP/opRJr9wYPGncX\nnbkO+261822aJI7WZIdYQwn/oIjFqJkJ1FpDSyW2N4bjFuOfDwzXdYJJjqGhE5UhOASEEPRVh8qV\noQtBv1to/YynvzVUQmUIBkZsPIERsrBxzrllsr8afFZoN/d2f4ZgqE74q2f4cJh+OI2OSYxSrWhj\nWXZX4x73vRCsD5PXzqkw+V6YXowSd9qJKkSGUhF0q4H11aFWNG0NPZIV5MTlNv1oCgltaZEk3gPD\nKg3PatzvDNtsASBlCHIsV4jU7nFguAP+VkgnqZd9NUGb9bcGLxN1Mx8IvvN9coLm+ymIREmmQso0\nJCASCSAcxojYkjnQ9s3h7Ph0NR8IZDjiAN9BqiIYCPGZjCMEtZG1hrYzAPNXBBva/dU9PRCMmIaR\nFiU6t+q/vxqcC7RLN0LQXxGUUK2hQ2PmfFw8NzkRa3XbhlK5q/bQ+18I+vyxp8L7YgWq6JqZh12D\nAaciWBBCMMgswYGFoG2zW5BNgCYenw7mh3sQWFgghbsi17BKwwkfrlQ6O8NKQtA6VGHyQ8Ktvts2\npBOWPCf4zkGbd3amXBbnMRRqHRvhUqsIHh/LQlAZxQSKOxNqJHWp3Xr7TvALRWgdJA+yEExbhypD\ncAjEZzJEKXnVoXKZvSG0CfrpSgiWSnXt/qoiGBSxGESzca8dGNjbKAe+Vss7Ec3NzrOLf0ZQEv6q\nq2douM6hcQpShIQSgnVMZcuNPQuKtsw+kSNChTBWrSJYKonWn6AYVAgWVnYpWL4cqrBNel7dbbpm\ncZG0IwRtwCpVhlIRXF0V+zK5XGtnWEkImgd1uXJq9zgoQiExV2DbkEojO4deH2xI0K0Gjo83ZkTW\n4xeC0gJRzQcGiisEo6moLATXgs9bLRbFdRwONxeC/ms8Y+7XCUFlFhMEiVyMaMiiVhG0bfa3q8LS\nfwj4DYA6VQRNfL3CyjU0MGIx0MdS6Jg18VU9CdYB3K0Gum7tzahWvWtcKxZEAcFFtYYODU8IFn0R\nEgWWlzu/98ESgpMqW65Xpp6eQ6dKjCJlIkPJEnSFoGG0Pw5Ep4F7fCQiFji7V7ekY3KJktL7vbCw\nwBiustewy1WODoNdMVSr1Ham2uVESkKwuK0qgkNC07xB/+RYRNq1HXRGsNu20HLZGxkK54/ERpOL\nqggGimGIc25kDKdiIM73zrYdaMUAvKDpiYnmGwFSa2hlV80IDoF4HMLxKBEqXm7kkDo9wAuMN4zG\n53ip5F3nejlPCd8BqjU0MGIxIJ3G8BtCFfKBtgR30xbqzgcDZKwDTuue4eq+PhwWF/FlCfYWIfFA\nCUEVJt8740/OE6FKnIL45SqXwTIDnRPspSLYLENw7/qudMxEJvhd7vuaeJxsXCz+RSuRxvFOcMHi\nIFpCq1Vxztot9KRqwclanYtkVC0aAsQVgqkJQ64IbvS/i2JZXltg10YxQKq4y4lyDB0aui42zkLp\nVC1fDqCaL0n31CBwhWAra3lJCJa2VWvoEEgkgHhcFgWV4TmHtqsG1leAJQMw1RoaGLEYkM0So+id\n8wBD5W3bW391NR8IjJm7HOH7pdD1ti2liv5JJiE1GSNBHpOQqLyf5lVFsB4VJt87RjpKJmUTd24u\nJuHAswQHEYIAe7flPtWJnKr89srYuLf4twiRPyx3ZTvcDaZJ7WbUrhoIdRXBg+WGXDklDoLD3blP\nTCYlIbh5EO373B8difOdTHa+niUhWNhuEP1q5zg4QiHnfCeTJMj7IiRKgWYJlkridyAUar1YlK7x\nwpYSgkMgkQBiMSdCwjnXQ4yQcIVgs2tWmgkt73LqF4JhVREMilgMyGTqKoIF9neD6e45OhKbuYlE\n+3u7tEarbKvxjhEy9UiGBHlwzR1PT9jZafTsqefBEoIqTL4vpqYsJ0IiNJQsQbdtpF8huHtPNjzI\nTdX5lSs6Ep9MOsGvWi1UPqjZgvV1YRSTTnc2D5EWiXt35YeIahcMFLciGB7PkMXbTLELJba2Wryp\nA93GRkBdmPzJuiwI1LkOHMPAmSHJS1mCQQpBf1tofWyEiyQM8ptKCA6BZBJIJIhQls71sIRgqyB5\nqKsAl3fq2v1VRTAoYjHAMIiGTU/82xZ714MJC+323u6vQI6XNuoqghFVERwiM09OEcJ2DGN04exj\nWayutn/fAyUEJx9VkQL9MD0XkYMqA8wSrFbFn3BYtC51oqkQXJeziSbmuxg2VEjEp1NEqHgucwGF\nytt299VAqBOC27dURXCIuBVBK51lEt9ioVRkbaW/XWR3sdBJ8ENdRfBolU1mvE9EDaaV8W+guEIw\n4dzLASgV2d4Mbh64U1topeLtTofDoB0eSOYhRjzc1ay4oj2JBJBOE/ULwUJhKELQsrxruVNFMFPc\nqhOCYSUEA8KNBDISESkiZu+90QpBaY2WX2uIgFIbfMNj7pEkJETXRy1CIn/acU7wgRGCGja5x2c6\nH6hoYOZCrJYlGHRFMIgMwd0teSGTO6fUQq/EZ7O1iqDtCMEgjAUOD0XFNx7vbB5SrXqLxJBmk9i8\nrYTgEHEX3HbUYNzw9Y7YNmvv9D5YUi6LBWEo1Nwtsh6/6E/tr7DBrO+HizI72/geRf8YBhCJENer\nnjiwbbZvBGMB7bpJh0Ktr3WpGpiGo3050D47rly+giCVAjKZmhC0YWhC8PRUrDeTyeZVYP85T51u\nkifhfUJVBAPDFYLRdERq9d+/M/j17Y8E6lSx96/R0sdr8vmOqPM9TObmgPFxYhS9CIkTJQRrTLBL\n+IIKk++HmctjxDml1nccYJZgL46h0GJGsO7hNvGQ6i3qlcT8mK8iqAVWEXRbDKenOye3SNVAPU+1\nYnkOc6EwoYje9YaBojM1IWjDeJ1wW7/au4OIPzuuU2wE+CqCpknqYFkWglElBIPGvXbiqbAcIRFQ\nqPyOU3jI5Vq3hUrXeBoOD+SHSDanoxgcw4BQNkXUuafbaFAYTmuoe05bbdL5hWDkVN5gSqT1ru4V\nis54QjDutYYC+yuDZ4W69/ZstvW17eJfo+mHspFfKqvO9zCZnwctN06cAmU3QuL0pKNhzANzSib1\nw85bGYqmjD86TZJTQliUiEKxSD5PIDNkvVQEbVvuP69VBI/lntKJy6oFuFcSi7mGGcFB3cZsu3Or\nmB85Q/BQNhVwZsZULEhwuNecZUF2Ur6G1m7ke/5+vcwHgu/+cXpKimOvNTQSAS3EjGrgCBT3fMdS\ndaHy9zo4CXRJN9e61CaYgcMj+YKu/z1U9IemQWwy7TjEOu3+xSL7u2bH9/aKe9/umA1braJXfb9r\nmkYy88AsQYdOTQiOxaXW0P3NwR3Au723VyrefT2kWWhHcjUyrTZ6hsrEBESmxkhwSpmo6AQ4OVEV\nQZepMRUm3y/jT84TpVKLkAgyS7AXIbi1JVoUQLShJBLiG+yW5K1IJQR7J3F+mhjiZJiOEBy0/Xd/\nXzwYEonWu8V+JCFY3lXREUPGXxHMzsqOyusrvdmG2vYAQvD4mAoRL3g4apDJeCHoimBwz3d8zJBy\nI7fXB7cHLpc7t4VCnRBMmhwWZOGXnYoO/LMoBEYqghEV51kIf5u9e4NXh+rpJAT9YfISuk4qpdZk\nQeHeL6O5lLTRM2gV2L8B38t8YNYocepvCw2FSGeVkd8wicche36MJHlMwuKZenKqKoIuKky+f9KP\nzhOjXHMisipVMIPJEuzFMfTePe/j8+eFrq8ur0uuVFo0ytik2nXqFWNJVH2B2ozg6spg14y/LbQb\nJCFY2lHzgUPGXxFMzcuruNUNvafW75MTIfpjse4FXO18Hx83uMOqttDgcSsGsfG4XBHc1QZu89/Z\ncVqMx0VmYSskIRg+lR1Dw2Gy4w/MkmToxGJgZKLYbpcHsLdW7PCu3rAsMSOoac03+6pV8XWAULnu\n7w6rebEgcf/9I7m0ZMC0dxrFrvS/2XN6KjZ6DIOO50sa3Ymc1BnFqDD5UbD41BixUAUNmzJRqJQ5\n3i239Xx4YO66U7NKHPRLKKozOV4h5rjNuYYxo64I+oXguXPiv3vvyj732USlYw+7ohFjKkMq5KsI\nWhZrt0sd3tUay/JmhvoSgqcbdULQUA+RgPFXBKNzExh45zt/YvU0I9qLWyh4C0gATk5ki3E1HzgU\n3HtsOJt22sAF5dPqwPPA3baAS9e4dVgXHRFR0xsBEotBJBNHg1oF+Gi3Elg+LIgNINsWmz/NnruS\nIVQo3xAmrzb3giOdFhX5cCxKKBSqtYdW7TCnV+91eHdreun0kIRg6FgWghHlGDoKHroURhsbI06J\nsuuxcNJ+TvDBEYJLymViEKantVqWoGgPDcY5dGAh+J4cgjWRraDoHSOmkUwI91V393j9bv9CcH9f\n7AanUn1UiBC5cqoiOFz8FcHK+AxzrHtfLBZZW+v+e/XTFupWoRLFPbbx7RYYhhKCQ8A933YmQwrf\ngPeAWYKVilgAahpMTrY/VqoIWgcqQ3CIxOMQSqeI1gWMH/TuA9WSrttCgUzoREVHDBFdF+fcsiAe\ns6Sq4P73RyMEpQxB7UBFR5wBFy9CeCJLnDxFVwietp8TfHCEoAqTH4iZpWgtf6qCHkiWoGmKRUQo\n1DlD8OBAzKCACMJ2F4pb78oN8BMqTL4vDAMSqRAaVm3RcLBTJd+7ZwjQe1so1AnBg2X5IWIoIRg0\nNWFgQzk7xTw+5Vcqsr7WXb9gtSoWfJrWfUXwBz/wPp4srynH0BHgbsjYqQwpfLNipSLbW/33hnbb\nFgqdhGBYCcEAicWATIZYnRAM0jm0JyHIkWwApiqCgRIOi2vcsiAe12Qh+HaHRPEWWJa37urm3i5V\nBM1dFSZ/BszOQnw6Q4JTijjzAB0MYx4YIajC5Adj4oK3i1xxnEMHrQj6oyM6+fj4q4GLi549/coN\nee5g/pxqAe4Hw4BwOkGcgvcAKfRWFXLxt4V24xbqIgnB/XvyIlFVBANHEoJ6grmYbzvXslh7t7tY\ngYMD8T0ymc5CwPnWvPqq9/pZ+/U6IagqgsOgVgGOGCR0n5OgZQ2UJdhtW6g/eBzEHHB9RbCb/ElF\nd8TjQDqNQVESgoO6Qfvp2jEUyNgHdRVBJQSDJBTyhcqndHlO8Fp/ofL+jMhu7u2SEKzWzflH1Pke\nBbkcpM8Jw5gyEaqEO2YJPjhC8IkeShOKBsYvTZJBLAxLRGtZgoPQb1vo+fPexyvL8k720qPKarAf\nIhEIj2dqVV8bIJ/vSwju7opqbzrtPZi6QRKC27caqkS9iEpFZ/ytoaWyxvy0bC2//m534qDXttD3\n3vN2mRMJeOrkuypMfgS416Jla8RS8qpu51p/ZaJyWbSDddMW6m8HTiYhfKxaQ4eJJwR9FcFigb0d\nK5Dvb5qQz7c2igG5IpiuHsgVQWUWEziuII9lop4LM7B3p7+sUPf8ddvSKQnB8lZdRVBXFcEREArB\nhecmSXKKRUis1/OnLN9tHR3zQAjBLIdEH1Zh8oMw/tgMaY7RsGozgsfHdGwdLBZF+2cz+nUMdecD\nAe5ty0pj6QNd9qYpJDQNYtNZEhSoZQnmT/sSgv20hZZK3u+DrkNs8y7rzHkHRA3m5pq/V9Ef7uLN\ntoUwm12qyxK81V2+XK9C8OWXvY+few7C6ytehiBA1FAZgkPAFYK2LRaKfrZv9xcKu7Ulvl8u17m9\nvz5DkIN6IajMYoIkHgcMAyNkekLQsti7FcyQoCvsk0lahoRL5zy/IQvBSERViALGFdbGWFxuDW0S\nKl8oiE3AdnSq+Poplbz1YDgM6Z3bDRFQakZwNCw+mmI8Lro+CsTBtll5r/Vi/YEQgpPRo+6CzBQt\nGX96kSgVYhSpEMEuiEViu6pguSxawN56q/nXu60IFgqeuAiFYGHB+UK1ysqxvHJY/IhSC/1izI0R\n59QLIO6jImiaoiIIvQlB9z0A6VgF7WBfrhJFIkoIBkwm45kLlMsQW5CV3PpK58pBPi+u40iku1vs\n5ibcuSM+DoXguWeq7G6Z0u51ZjKiMgSHgHt+LAvCGbkUs73cX6yAe//vpoJbLwSt9U25YhBRFYMg\niccBTcNIRuRcuRvBDAm6bb7tFvfSOT9cbjAAUxXBYKlVBCeSshDcNb0QZsS5e/lluH69/ffrRQhK\n1cAx0Fbq5/wNtQwfEbOzkJmKolMRQhAo7bfOEH0ghOBUttz5IEVboufnyIZOiDvzBqZpQ7Xa1jDm\n+FgIg6Mjr9rjp1sh6K8Gzs0JsxiA46srHPoWEno0xMxFtYLsl/jcuFMRdIRgqcTand6und1dsdDM\nZr14gm5wxQHAYuoAk5BXJYpGQdOUEAwYXRftfG67XnVW7prY3Al1tJr3x0Z0mvMFuRr4+OOQKW6x\nYft2DPQIs/PK8GkYZLNip962Qc+mREyMw/ZW7+2CJyfiTyTSPkTeRWoTTMPJrS0p2D6Z0buaQ1J0\nh7uZYqSjshC8O2BWiEM3IkESgvt3G/JClRAMFldoGQkdM+wtrPbtMbh1q/baPXc7bUYHu2n99VMv\nBFmuF4IxtdEzImZnITGXJUmeEs7vwcmDLgQnVJj8wIRCzExUnQgJjXIXERJ+Y4BmA+r9CEFpPvA1\n+S9fyJy0bFFRdMZIRkgkQ86MoFigrV3rbdHQT1soyELwYnydbaa8xUs0yvh4b8JS0ZlwWAhBtz1o\nO3GeCbzSrF0odXQGdq/rbtpC83nZLfT554H1dbkFWM0HDg1dF4t2y4LEZFyqwm7v9S6+3Xv/9HTr\n1kA/knFIBg7vyA+F7GSH3lJFT7hCMJoxsP1CcK27lu9OuOezlUhoMAfavqUigYaMK8oNA8yot7Da\nIyeV/9ziYKXiy3Oto5vWXz/+NV5TIRhTWcCjYnoa4osTJDmhUjOMad3+/0Asmyfn1DZjEEzORUhy\nCmjOnGCB9fXWx3crBDst8FvNBy6/Jc86nJvpP/dOIQR5IivKra4I21wudx1AXC6LCpGm9eYWallw\n9673+kJoWc0HjgBXCLoVwVVtkQV8NuPFYtvr27K8XeBuhOAbb1D7XZqbg6UlYH1dOYaOiHDYacm0\nwJjKgK8alz+1e4qKsSxqmwTdnq/61tCDFXlhkp1VWb9B4grBSDYhVwS3WptGdEs31aKTE2+TKZEA\nfWNFnhFUQjBw3IpbNAqm3loI+ju0Dlt4gvXSFgpyRXA8VYHNzYbWUCUER0MkAlOPTzKOOCl54nD6\ngAvBqaUerAsVLRk/lyKDuGuUMaBYZHm59fH+nab6EFt3LknT2gvBSgVpTm3J1722fE3e2Vw890D8\nOg+NeFw4h/pd5qzTQq3K14lbt8R5zeW89t1u2NjwHkyZDIwf36sTglElBIdAvRDctKaYxlcCLBVZ\nW23dTeG2fieTnc93fWTE8887raRra8oxdESEw2JRZ9ugjY05m3oOxd6yBPf2xL05mex+oSgJwUSV\nwy154y47r/oEg8Rtu4yOJSUhuB+AV0w31SKpApw0YWenQQiq1tBgca9FIQSjuFf0PuPY15oLwfq1\nmcsgQnCstEnJjlAmWvuBwnqoK2NARTDMPj1NzhGCBeLSjGg9D8TKWYXJB8P4hTHGnF8stzW0lRD0\n7xhGIrKjFHSfIbi6Kr4XiEWr/8Gxck+ea1m6pO4ygxCPA9ksSQrewuG0O8OYw0Mh6EIheOSR3v5e\nf1vohQugbW6odsEREA6L68/dlTeNBMmIr/xrmqxcaz1X4C7su3F6bIiMeMr5QkNFUJ3rYeEKQcsC\nEgliId/CwDTZvtm9xXwvJjEuUjzM6QaHtu+5HImSnVCtoUHiVofC42ksNxIIsUHbbGa/F3qeD7QP\nwba91tBIBLSQqggGTDzuuQPrRrhmGFMhQv5dr7XKrwmGIgRPVurmQUU1sJs5ckUwzC7qTE6IxXOR\nGO22+R4MIXhZRQoEwfjlKcYRPZ4VIlAQrWPNWgfdamAiIYwkQG4P7TY6otV8IMDyprxwWHxKpREP\nQjwOjAnn0JoQ7MI51Lbh2jXx8dJSb9mB0CgEG+bGVEVwKGiaEO61qqCmYeRks6Wb328tBF1h140B\nQH1kRM0UpL4iqFpDh0Yo5Il2G41IQr5/bl/tLnS6XBamUJpG1zEftt1oHLKP77kcM5SRRMAkEs51\nlkhiUPHu6dUq+7cGS5XvWQhWdqkSlipEoZCa+w4aw/CcoON1ofL717ZrH7vrr1BIXM/1beHVqvhc\nKERXVVvbrpsRPLyr2kLPmLk5yM0nnMi3qPS7UM+DIQSfUqFUQTD+xBwJihgUqRCFYhHLElW7etz5\nwFSquRDsxyjGPx9o27ByIN9Zlj6iVpCD4FYEE+Q9c4FCnrXl9jMlq6tC+MdijWK9Ew3zgReAjY2G\nKpESgsPBFYLuLI8+KW+a3bxm1lpH63EXep0W8A2REc95X7PWNtjC5yxkRFWG4BAZc/bKLAvCSfnm\nu329u55Bf3Zgty3ghYK3YWgYYGzcZY157wBDbQAEja4LMWjZGnHDkkXBWysDfe+ehWBpu84oRkQJ\nqApRsLhCsFqFWLYuVH69CIUCtu1VBN3Z7vo5QXf91q1RTKHgictIBJJbt+VoGOUYOnJmZyEyN0mS\nPBV00cXXgvteCCbIk7i00PlARUfSTywRo0CSPFXCWIUiYLPS5JniVgRTKW/xcXDgzSN1IwQtC6n1\n1C8Et+/mKfnaG1Kckn1CnedBiMeBaJR41MZyjSRsm7UrrReIpRLcvi0+vnSpu4eGn/X1uvnAcZpU\nBJVZzLAIh4Wxj3tdFsbmMPD6xg73zJZGT+WyeOh3yvx75RXv48cfl4Xj7nJezhAc11WG4BDJZMTi\n27IglEx41zmwfad19ddPP22h/s2eXA64d49VfPfr328JiwAAIABJREFUWMzLh1UEQjjsCEEL4glN\nEoJ7VzvYAbfBP/bRrlokh8mvqwzBEeAKQdOEeCKEGfXac/bIwc2bNREYjXqb9PXtob22hb75pvfx\n1BRoq3WtoTGVIThq4nHIPjRBlkNAo0jrxfZ9LwQnjZPOZSdFV2izM0yFD0hxAmiUrRBUq03nBP07\nSvG4OAXVqvf5bhxDNzc9kZBOe4ISGqMjFlMHaFE1YzII7gI8ntElc4G1a63dpm7eFA+dycnussTq\nkWIjLorFRWl9Tzy0HEKxaE8upIruCYdlx8+9+AJL+Mrw+VN//FSNbquBxaIcGfHRj8pf31iT53xn\nl5TD8zCJRsXmnG1DciwiR0isVTq+380O1PXerverV72PL12iUQgaSggGjb+tL5YMy0JwgFB5f2xE\nu40/KTfyeF02ijGUY+gwMAzxHK9WHUEY9XbV9hmHa9dqayrDkDfp/fQiBE0Tvvtd7/WHPwwsL9dV\nBFVr6Fkw+8wsOScS6oEWgipMPkA0jdy0TgZxhy9jQKEotW+CWGT4W0OhsT20m4pgfVuov41k+fvy\ng+zcVDDZSA8y7mxffCwmC8HlatP2wP190SbWj0GMS8N8oGU1ZFNOL0RU0PSQCIfFn1ru4+wsE/iu\nraNjbtxofF+384FvvincJUHMk/mr+pgmG7vy5s3sRVUOHCZSluBEXMx6O2xvd3YNda/NmZnuq/+m\n6c0QAzz2GBRvr7OLpyRDcdUaOgzc528sI7cJ7t/t3hionm5FguQaenBPZQiOAF0X4t+2HedQX0XQ\nFYL+imAi4Zn5FXxLqF6E4Ntve6I/lYIPfhAVJv9DwtyjGbJGmRAW1Qe5NXRq0up8kKJrxhcSZCXn\n0EJDRbBUEg//aNSbIRlUCDYYxbwrtzEtLvbyf6FohlsR1MfThPHmAguH5YYZAsvyYonOn++v6G5Z\n8jm+cAHY3WXd9JX/wmHmVJVoaISdIkFNCM7Pk8RXAT495da7jZtp3VQEbVuOjPjIR+pmgnZ22LB8\n51rXmV1U53qY+CMkktNJuSK4376jwrZ7zw4E0Rbq3u+zWWFisHa7KB0zMx9Wmz1DoBYwPhaTK4Kr\n/W+cupu87URCgznQ7m0VHTEi3CqfYYAZ8R7M+4zDW29JFUFN8wyk3Gd8pSJEYTdGMbYNL73kvX7+\necegaHm5rvXb6CprVhEss7OQmYgQwpQ2/eq574Xg+UUlBINkfClFznEOLbXIEqyvBoJ3czo8FCLR\n3ZVq1Rpq262NYgBW7soGJkuPKPuxQXErglo2SxrfU7xJhMTurpgTicflbMde8M8HZrPO78jGRsN8\noKoUDA9XCNYMWuJxohN+dWdz8zW5+m5Z4hrXtPZC8NYt8XsC4jr/wAfqDlCOoSPHHyERm05Llf/j\nfKgm2JpxeCgWiYlE97NDILeFPvaY+L1ZXZVdQhYeVuMbw8A9T7FcUhaCW02svrvEFXjtfgcazIG2\n68Pk1czYsHDXWtEomJG6GcHvfU+qCPqPd9tD/eu3TmY+N25QyxmORh0jsEIBdna4zUXvwGhUbPQq\nRsrsLGRmk4SwqNB6p+2+F4L/4DcfPusf4b5i/NIkOfYIYYnW0NNTDg/lNhD/fKBLNCpeWxZsbwuh\nZxit24uuX/e+p2H4KhYOy+vyL/Xik12EmSnaEos558NxDq2RP20IFncXA720iNXTkB+ooaIjRowr\nBP0zmNX581Lm0L3rJSki5vjYC5MOt3aklqqBzzzTxGGyPkNQ5UUOnWhUiPdqFbR0mhi+am+1ys6d\n1vPAe85+QC+zgbYN777rvX78ceDoiNUT3w6CprFwqcfMGUVX1ALGc0lMv4PkSUTuBeyS7W3xtk4m\nUX4ncdcATJnFjAa3+0rXwYx4G+R75OC99yjti2e7uwlfLwTddVc3rZzf/rb38bPPOpvJKyvYwB0u\nUPuLtJASgmdANgvTD6V4nPf4GC+1PO6+F4J88pNn/RPcV4w/f5kYRZKcUkGneigWDv7qXbOKIHg3\nqPV18d9W7YTlMvz5n3uvn3pKFhvVKqwfyG9efLZOKSp6Jpt1FvbJJLqG5yhomqxdlXtDe8mQa4Xr\nNgp4D4n66AhDCcFh4i7mLMsTapW5c7W8UIDqwYl0fXfTFnp4KELkXT7ykSYHqTD5kZNMCnFQLgOa\nRsSQlwDbb200fyOeEBzvIZZ3bc1bWCYSTmfH8jIr+Hr5jRiLS/f/UuQscNv+jHiYql4nCpq5QLWh\nWvXGAS5ebL8BePOm9/HFCzZsbLCJLxfGiPb0e6ToHvffNRIB0w5jx8Qmyz7jYFmU3hYn0RWCyaQQ\njcWi+NNNxRfEte0+w0Mh+JEfcb6wvMwBY55ZjGFgGKjn+BmgabDwwUl+lO/wFFdaHqfuvoqeGP/U\nB9DQGOMAE51yodrgHOqPjpDe69ygXBHRqi30xRe9YxIJ+Oxn5a+vroJd8Czup9gm9tiFvv5/FB66\n7jhIahrJbMgL/wXW3tqtfey2BkL/QtA0m8wHgqoIjpj5efEQ39vzVXoWFpjAO98cH0sLu242AV57\nzYukeOgh4Spbj7WyJmcIRg2VIThk0mmfEAT0uC5Vf7ff3W36vnJZXPOhkOze3Al/W+jly454aIiO\nMJRj6JCoVQSjYOqx2rneI4d9vYkLVBtu3xa/B5lM50W9X2M+NHkEpZJ3zkNhCOvqnA+JVEqcb10X\n92AzJW7U+4xjA+V3hBB0N/7q5wS7NYrxzwY++aTvvrC87FUDAYwY58713zmkGIzZZ2ZhPAeXLrc8\nRp0aRU9EJjKMzRi1hWKBBBwf14RgteoNGte3jmSzcs95s4rgxoZsRfxTP9X4fVauHIHp9aothjd6\ncy9QtMRtEUyNRWUheN0z5zk5EWLQ3Unsh/V1bzFamw8E7PUNOWhazY0NlWjUu3Qi7iz53BwZv2FM\nocCtt7zz36kiWK3CG294r5tWA4HdO8dyhmC2cyahYjCSSXGeIxHHOTQVlg1jbjZ3k3RNvsbGelvQ\nNbSFgoqOGCHptLjGNQ1CES8WqIRB4V0v3LFcFtfsnTs0dYg+OhIbsJomBH272bHjY29uLByG87ro\nE63d1x0Fos75cHCzBMFpD00IRVcmSoE4pSs3a8e5uM/frS0xtx8Oe9+jGfv7cMVXYPrYx3xfXF6W\n5wNjBhd9LxWjZXYpAl/4AvziL7Y8RglBRc/MPZZlgh3ApoSBeegJQbcamEg0Pixc63KXeiFoWfBf\n/6v4L4gq0Qc/2Pj3L78p71ovTeQ7TzUrusKdxUxMJWUhuOKZLgXRFtosPxDgZHmfPJ4aiCZ05TY2\nZFyzH113DB4iESJT8sztze+KlV03QfJXrnj3gWwWHn20+XEbd0vS61lVDRw67gZdOi2MX1JZWQje\nfLd5lqDbFtrLtbizI/6A+H156CHxcenWKtt4Q6lazFBV/yERi/kDxpEMY/avem3A+/tC7N25I6q4\nls9jz7a9+I/FxcZOn3r81cClJYjubWCheULQEM+V+fkmb1YMjJslaFni/Jtxb9G1zSTV926iafKS\nya0IuuZe6XT7JdV3vuNtGDz8cN0+fL0QNAw1H3iGdLORroSgomfmPzzHGEeOE1GEymG+1ubXaj7Q\nxT8XUC8EX3/dGzIPh+GFF5rfjFauHkuvl+bNxoMUfVETgjNpKj4huLvnOXx2Gybejob8QIf1ZdnN\nbm5OafxhE4+LSnAu57UF2bNzUsvgzXfEye/m3L/yivfxhz/cuoKkwuTPhlTKaw+dWJSzBK/erHf0\nEQu+fuYD/W2hjzziVZzXr8n37+kp26tGKwJFChhP6ZLo94fK+91it7ZE/qfbsbG6Kp7rsRhdLej9\nQvDhh4H1dbaZ8v7uaJRstnM0gaI/3IpgLVTe8P6hN5mB27e5e7PK7/0e/Of/LARjOi0bf7VrC83n\n4Xvf815L1UBo2hqqKoJnx+Rk584tJQQVPTP/yYfJcEjYySapHJdYXRU3lFbzgS6thODxMXz9697r\nT3yi+VwRwPJtedd66eHGxYuiP9x2nUp2gihlTwzk8zWTH1cMZPs0am05H4hnJOSixMFoWFoSC7Nq\n1dlJPjeNjafA9zYrHBx0rgavr8PKivg4HBZOcq3Y2JYtR1WY/Ghw5wRLJZi4PCGd5429CAc78mbM\nyYmoHhpGb4v3pm2hwOod+f69cK6N9axiIGIxTwjGMhE5QuKO1wbsCsGFBfGeoyPRKrq35xmCXLrU\n3iUYxKaBNB/4ELC+LrcCR9VM6DDRdbH+sm2nIhiKQFT0gW4xDdUKb39zj2pVnKt79+Q5QWgvBF9/\nXdwPQFSb6kWefa9OCMZURfAsCYcbXffrUUJQ0TNzn3oUI2yRIE8VnWrVonxcYnOzeXSEn0xG3KjC\nYVkIfvnLXsVpYgI+/vHWf//yqvw0Wny8h1ArRVvOnRO7iPnoGEnyXrWgVGLtxinFojhPut5+hqAd\nV696u81jY7L5xPquLOrnHlLiYBRkMmKTxq0KhhbmmZQMY464datzRfBv/sb7+Ikn2rSRWRYbB7Jb\n1OzlAUrMiq7xVwTD4xlyEd88qGVx5S/vSse784G9tIW6M2UgKsKXLnlf80cLACyq6Iih4VYETRNi\nYzEpQmJ/o1QL+3OFYC4nNm8yGfG5H/xAvHdqqrvYkO1tr6sgHndMZTY26oRgVAnBIeOKumhUnD/S\n4kb8N3yKDWY4uePd292MYP9zuJUQNE254+NHf7SxY2d3OS9FhcQyUWUCdsbMzrbfuFdCUNEzibEo\nY4spshyhOUGVlYMT7t7tXBEMhUSm2DPPeC1j6+vwzjveMS+80LqUfXoK+4fenUenyuzTU80PVvRM\nJiN2j6pWmETMkucEX9+QhEA/LZu7u2IO1OWy38gqn2ejIOeLzT2shOCoOHdOVOGPjsCemiYT8gxi\nKJe5/vJu2yD5lRW5CtTQMuRnd5cNy7dNGdaZPd/CRlgRKK4QrFTARmN8Ql4GXPmGXJbvZz7Q/3tw\n8aJv08g0WdmVhd/CEyoDdliEw+Jc2zbEkrocIWFla60ZrhCMxYR4eOYZzzgsHBatvd0gxUa4ERPr\n67IBmKGE4LCRQuVNICWU3bf4OL/D/8zLbydYXha/F64QdIVCu03eK1c8oZ9KiWgviZMT7hz52r40\njQuXDTXecca88AL82q+1/roSgoq+mH88S5YjwlhUiVA5LHDzprjpGAZtZz7chYiLP3risccaWw38\nrKwABW+gYZ41wo+oBvSg0DTv3z+aNmQh+PbeQG2h5TL80R95ld/xcfjxH/cdsLHRGB2xoG5RoyKX\nE2LQNOG0GCY6Kau9qy9utQySt225tfuppzoMqasw+TND18Vmj20LMZicSUnzoFff9O6vpinagTWt\nt/lAvxB87DHfFzY3WbV8J1rXWXi4RaCsIhDc8xaNghnxVvh75ODGDWzbuye7XTqhkKjoP/GEEIWt\nop7qaWgLhaYVQWUUM1xcIZjLyRVBG40yETaPYnzjG/DFL3qzvJmMuP/7q/f1vPyy9/FHPtKkVbiJ\nUczFh5QKPGs6OT2rVZaiL+aeWyDLISFnTrB6UuCGE0vUyVWsns1N7+Nz59ofu3zXkibbF1lprxwV\nPeM+CPRMQhaCN/N9O4batqgEurbiug6/8At1O4/1O8cqYHzkfOhD4r+Hh6BNTWL55seuvS1Wi83O\n/c2bngFQKASf+Uz7v6cxQ1C1D42SiQkhDMplyF3ISrNj796J1RwB9/fFtZtOdx8VUyjIZlCSa2xD\nhqCKjhg2khDUPdG9zzjcvEm5LOaCIxF5Ya9pYsOgU56ci2nCXV9X8cMPOx80zAiqiuCwcc/55CQ8\n/7xwAQewCFElglYuAzYHB/DNb4prVtOEeG91H15Zkee/n3uuyUENRjFqPvD9gBKCir6Y//RlMhyi\nASYh7Hyed6+K1UOvbmCuOIDOQ63L7xyC7bkNLsV2etuqVnTEzYnSswlKPiG4smy3bQ1sxyuvwFtv\nea9feKExlNhe35CrRCpMfuScPy+uwUoFmJ7iBO9ivr0SwTQbz71twze+4b1+9tnObYQ71/flDMGU\npTIER4g/WD79yDQGXpRH/qjKvevidT/zga++6sUPLC7Kvy+VW8vyBoARU9f4kHHPXSQCpr81lBxc\nvdpQDeyX5WVv9nt83Hss22vKLGbUuP/2pik2dv/kS3F+PfXvWWKZChE0LKiI+dCtrUaTtmb4852f\nfrrFOq+hIhhTQvB9gBKCir6Y+5HzZA3xBLEIgWWxdjMP9FYRtG1ZCHaqCqxckQOPl+aqLY5U9MvE\nhFi8JaZERbDqVAtW9+KYZvPWwHbcuwdf+Yr3+sMfFu1G9ezekMVBOmn3XF1WDIamCQMAgGJ2ljxJ\nTklQJUzptML2ltUgBK9c8RYSkQh86lOd/56NGyfS69lc8/w6xXDwG8ZoqRQLiQPvi7ZdM4zpdT7w\n9BReesl7/eEPy19ff2dPcimdGqt03Xao6A9XFOg6mBGj1ga8Rw5efVWaDxyEpm2hxSJ7hyFKuCdZ\nIzkWGSh6SNGZbNbLha1WQQtp/DfP7/Fr/Bs+wquADWWxfjs8lCv4zTg6kgPkf+RHmh9n31vmLue9\nT6gw+fcFSggq+iKR1Fg8H0bDJoRNBZ3DzRLFYm9C8PDQm0+Ixzu/d/lGWXq99JAKoAqaUEhUhiK5\nLDqeYUwxb3G4W+3pIX5yAn/8x16FYGEBPv/55seu3ypIr2cnlTg4Cz71KbEhE5/JoIdsihgck8Yy\nbTbf2ZYqd6YJf/VX3uvnn++ulawxTN5ucaRiGPiFIMDMgtz3efWb2xQKomVM17tvD/zmN737+dQU\nfPCD8tdXr51Krxfm5CxJRfCkUmLGLxwGO2rU2oD3yGF/702Kx+I+O6gQ9BvF1NpCG+YDI8wvaMo8\nZMi4WYKmE7F8fAx86ENsMEeWIzIc1S5+24bvf7/99/NX+S9caD3/vX39gDzeAyKRDreMAVP88KCE\noKJvlp4aI80xGraoCuZP2d315r5KJfjqV0VLgd1inVffFtruAWHbsFJvPf6oSqUdBg8/DESjGOFK\nrT20Spidd3d6EoJ/8Reey1giIeYCW80ard+Thd+cmg88E3I5Z1ZkVmNx7ASLEJpTRzi4uiEd++ab\nwgkWxEKyrVOoj40re9Lr2fMqC3SUGIY4z5YlKgbjF8ekr199y5RC5LtZuB8ciAWjy2c/22hSsHpX\n7uBYPK8yBIeNmyWoaRCJ6TXDmCo6JyWd4lvXa8f1S6HguU/6DccahaBqCx0F/lB5EBuy1Q9+mG0m\n0bCYYI9oxduU8Vf76qlURHagy/PPtz729k15Y+fieVuJ/vcBSggq+mb+o4tkEa2aQgjmOTryFg1f\n/Sp8+9siI9C/W+jHbxTTqS10bw8Kx95CIkGe8ceVWhgGbrBwPAYFxMLBJMzutd2uHUOvXJEfMD/3\nc+3dRjfW5d2CORUmf2a4tuCTc1EsdLIckuKEkzs7tWMqFXjxRe89H/tYd9mS5sEx37olD4bN/ojq\nHxo17q5+uQzJR+TzcWvVqN2bu20L/eu/9ioQS0t1JjEOK+vykmPhshoMHTZulmC16gSMJ72dvD1y\nFL8nbCMHEYJ37nibvfPzvvuAMoo5E/yh8pYlNmNLT3yIXSbQMcmxR7K8j6aJk7a87EV/1fPWW5AX\nUz+MjTW/rl1ur8gdWhcuqw2+9wNKCCr6Zu7Tj5JB2EjaaOiFY0qnoqrjhtG6tBKCPRnFLCM5hi6x\njPaQWkAOg9lZsXhIpEIUiGMSwiLE3WvFrhb7+Tz8+Z97r599tnMW1dq2/BBRGYJnxxNPiP/mzqcp\nEMciRISq5Ar4yityplS7nWI/f/J7N7hu+34ZEgme+mQPbiSKQHAt/EslKE6fYwGv3cI8zfODV0WP\nZzdCcHNTvt//xE80ryKu7spqY+FJZfQ1bKJRMddtWULsVRNen+8+4xS//x4wmBD0P99r84HQKARV\nhuDIcDt3qlVRESwtPcJeeIYwVXLsYZinRENiY31vr7lhjG3LJjHPP98misC2ubMtd2hd/ECXPeWK\nM0UJQUXfzH9gkmxCbAFXiJLimKNrYhv5+993nAcdnNzaBnoxirl7FylDUEVHDI+ZGbF4GJ/UqRCl\njBBp338vVssSbMdXvuLtMKbT8FM/1fk964ey8Ju9pB4iZ8X4uKgKZy9No2HV5j6293WO9yqcnMDf\n/q13/Cc/KRacnVhbg//0R/JOwqef2G6bXaUYDrOzYm6sXIa8neCR8d3a16ro3PruJslkdxlyX/+6\nVxG6fFnMGDeQz7Na8KtKjYUPTAz0/6DoDrcTwzDAjHmD+HvkKL5zs/a1fmlqFAOwsdEQCaSE4Ghw\nswRNU2zY5Uth9nMPE3YqglHKGKYo9e3twepq4/e4c8dbo0WjXrxQUw4OuFPxnetQiAtPq2f4+wEl\nBBV9E4/D0pJ4+oug0igr7xxg23JPOYjdpkqd94dpwo7XadaxIvjayyaUPCF4gTsob+LhEIkIMTj/\neAaDEic4gbRHx/zlF8tt33v9ujx8/sILXew2myYbeXn4cO5JVSU6S558EkLZNFk9z6kbI2FZ3Pza\nLb7+ddkUpN4dshm2Db//+1Da8RxD0xzzhX9uDuGnV3QinRaVXNcwZvERIdBtRDv4vXdOusp2vHNH\nXPMgqoCf/Wzz46q3l+V4GMNgfknNCI4CVxREo2BGvQ23TWawbt1BN0td50TWs7/vuctGIqIt2KUx\nOkIJwVHhin+3Iri2BnZOtIYussI4B0TLJ7Vj3n678Xv413HPPNP+OW7fa5IheFENCL4fUEJQMRCP\nfsDbRiwTZfmOyb17cqUPRFuKO0zusrvrzZRks+13JEslePVbstPg85O3uhtKUvTF7Cwk58c4F9mk\n4hjGhO0KX/w/9moOYvWUSiI43uXpp9vPFLhUN3bYxmcvpuvMnle+8mfJk08CmkYua1F02oMBXv3S\nJm++6R33uc91Fyfyla/AG69U4OS49rlf5d8z/kKXDjOKQInHhUAwTfFn5ukpACpEMAmzfM9icbH9\n97BtUQ10+cAHWnd2bHx/U4qOmEiVBnaqVHSH294bjYKp6RATz80v8tOUrRCxu+/1/b3dTQAQlWC/\noDxaPvQ2kQAjGekpk1LRP/4sweNjp+KXyxHGZJ51HuEGRvGwdvw778jvLxTg3Xe91502+zbe2vbF\nhEA6aalz/T5BCUHFQFz6xBxRRKmvhEFxvyjZyfvnRJaX5ff2YhTz+utQOvKqgVNsd5w5UwzG7KwT\nE7JgYxJGA3SqrN/I861viRmxV1+F994TIv/4GL72NWqto8lk66iIejbf2ZEXifFCV62GiuGRzYrd\n/fGZKDaQJ4EN/L9/NVZrA3zsMZ9VfBv29+Hf/TuEtaTDs7zB555eQ/mLnw2aJqq5IKqCqQ88TJhq\nzRyqcFiuBcq34rvfhZUV8XE4DD/+462PXX3nQHq9MFFscaQiaFxRMDPjOElmRMveMkt8iReIXftB\n6zd3wC8W6jf9VldkA7CFxe4caBWD455zf0WQiQnCVJljnYe5SeT0oDbzt7LizXyDqBC6rqPz853X\naLd/cCy9vjh1os71+wRly6cYiIVPXyLDVXaYoEwUs7DPq98qMr0ktno/8hEhGKBxTrAXo5iXXkKa\nD/wxvq2MYobM7KzYQU5cmGL+zip5EmiAtbfH7/7uQx3NQT7/eaTMuXasv3sovZ7LtrAwU4yUJ5+E\nVy+m4UqFU6c9+M5ahokrZR77QLSr2U+Af/tvnUXGvhADBiX+R34f7TO/MKSfXNENfufQ49lLzPF9\ndpgkjEm0eMyVV074xOebh7u+8oqo8rp89KNeC2IzVm8UAG9maGFeZUeOiglnFDOVEjOcp++mYWsL\nixB/yyf5zHde5Kk+vm+xKIeR1wvBtU25VWD+gtrdGxVuNc6tCJ6eAmNj6JrNnL0unuflEtFwlaKl\n1wxj3MzQ733P+15tZwMd7lyTR0YuLFZbHKn4YUNVBBUDMfdIkmxS9HeWiXJMmr2bYrE3Py8WBy7L\ny3KeYLcVQcsSMRT+lrKP863uShGKvnEXiYlH5plmizAmNrB7YvDuO2Zb05jHHnNaC7tk46Ys/OYm\nVJj8DwNPPAGzz84xGTmiSIx9xgGbl//6lMuXu3OU/Nu/FdECAG6J6Z/wn5hnHT7zmaH97IrOuM6h\n5TJsHceZyIkbdJwCGnD16ytN3/faayIj1GVpqX01EBozBBcuRFocqQga9zqtVuGDH4SZ82Kj1nKW\ngH/wyo/yu78Lf/qntGz7b8b1697x8/PIGbOWxeqePLqxcEnl/o6KsTHh8FmtipGNzU0gHCY8lWPO\naQ0FiJoFQMx5uuM7m5vex7ruxQm14849ufx34SE1//t+QQlBxUDE4zA3K54ENhqHZNm8W8S24bnn\nxE6kWxUqFLzwaei+Inj1qtNRdiCqRgnyPMOb8GM/FvD/jcJPKiXaO8fmEixOlrjMexyR4YQUdrHI\ne++JHeBLl5x5QucZPzMDf/fv9tYCtHa3PkxeVQt+GMhk4PzFMJ98rkAYExPn4X54xF99w661DrXi\ntdfgX/0r50W5DPlTLnON/5Y/EauUT35yqD+/oj1+IXjzJiw9pBOhQgRxYq++ctLwntdfhy99yXu9\nuAj/6B91do1d3ZAXhguXlSgYFYmEeFabpjB0+cJvpNCwa0LwXnGSr/1llR/8QLT6d4u/LfSxx+q+\nuLPDqu3LpwzrLFxUFcFREY97ofK27Zn15S6kSFDgHPfQqWI4hjH5vDfv6a8GPvZYd1YMtzfk9p+L\nT6r4p/cLSggqBubSE97OrkWI2xtx3nhDVIQ0TXYRc+cESyVvXCgUaj8m9K1vARWxiAR4npfRQ7YS\ngiNgdlacn7FL01ziJvs4W8uFAltb8LM/C//wH8Kv/Ar8+q/Dv/yX8Ku/6rWXdMsbN+scQ1WY/A8N\nTz4JmY89zUe112qfG69ucu27e/zhH7Z+3xvmpUnoAAAgAElEQVRvwG/+ps8t+GCfEBa/zv9KGEuE\nS7brJVQMnYUFcX1XKqItLHphgTiF2tffuxGSKkRvvCGbQS0sCBHYTfRAQ3XoaeUkMSr8ofK2Dece\nTfD3576JRQiTsIgLuVbhzh0x990N1apsFNMgBFWG4Jmi62Jz1ra9Wb9QCBaeEMODOiYXuU204A0C\nv/222CzwZ4J20xZqWXDvQH6GX3hWXd/vF5QQVAzMJ34mR8K3eKBU5O4dmz/4A3GDaCYE/dXAycn2\nroMvvQQcejNkH+Ml4WWcybR+kyIQ3PZQHnqIChGiCOdWo3hIIgHf+IZ8fMuw2TbcugXvLXvVAQ2b\nZ59Tt6YfFp54ArRUkktPGZxjmRglkpzCygp/+IeNbnMg4kN+4ze8aAIADg74Nf4Nj+CkT6u20DMn\nEhEzYy53IpdJ41UBCwel2gzY974ni8D5efjH/7i7IHKzarN+Iu8OLXx4tsXRiqAJhbzHpTsz9j/8\n5DKzbFAmioYNpRLf+Y4QA9vbnb/nnTve9T0+7hkP1djYaIiOmJ9HMULcc+5G/eg6zP2Y563wCDeI\n7G8TQoz3rK6KLo68iBckm+0uqnlt1aZc8naMxjhg7Eml+t8vqNWWYmAufOoCnzG+jYHzVLAtUqFT\nvvxl+J3fgTlfd4hrGNNtW+jysiMenbbQMCbP87JqKRsRNSF4/jzhEFzmOjom09VVQqUC/+W/yHOf\n/fDlL1XwDxx+mNeZ/tyzg31TRWC4BhPa8x/l43yLh7gp/F3397BP8/zrfw0vvihayo6O4K234F/8\nC2/x4fIF+3/jBf7c+0SnoTLFSPg7f0dUDjQN4uemsAhjuQ6+xSJXv3vIa6/BF7/oXetzc92LQICt\nq7uYttcrPqafkJjLBvx/omiHP1fu+Bh47jl+kf+LKro42+UypZK4frupCta3hdaPApze2eYQ7xzr\nht7RFE4RLO45d+/F4TDMfepyLX/5EW6g2VYtT3BvzzfPjdhv72Zz986bB9Jw6YXIqveXK37oUUJQ\nMTBzCyEmL6b5HH9JnCIGJYxT0W7w4ovwH/6Dd4/Y2RG7Td0axbz0kvOB00f6DG+S4hQ+8Yng/0cU\nDdSEYCQC587xOFeYZhMdE/b3uXEDrlzp//tXq/DVPz4C23uIfH76DRFIpfih4e/9Pfipf7LArzzz\nMr/Fb3tfWF1lfR1++7dFe/DP/Ax84QvCTdDPP/v7u/zs+r/zPqHr8PGPj+aHV7RlYQE+/WlhKKLH\ndGJZwzEFEvzZ/3kozQTOzcEv/VJvEa6r35ODZRczxy2OVAwLtwvbFYLFp55zTEOcCr2jFpaXRUW/\nfiPHj23Ls4QNbaHA2vU6A7Bcqa+OEUX/NBWC8xr8gnBrdg1jjJM9QAhB/737mWe6+3uuvy67fl/M\nHamckPcR6rJUDEw8DrnHZxjngM/zl5znLhx6mVGvvSba/1xWVrqvCL70EmBWa46hH8NRhkoIjoSJ\nCd/8z0MP8d/xf/PjvChe74uHxx/9Uf/f/zvfgcNVrxUtxQkf/6wKkv9hIx4XI7nn/qdf4KO8ys/x\np+ILG+vi+mzDP/2n8PPJv5A/+dGPilKj4syZnoaf/mn4+Z93Xs+FHVOoJIdk+Op30rV57oWF3kUg\nwOoVeaG4MFVucaRiWNQHjBcvPc1uaIbz3CaMhW6WiP7/7d13nFT1vf/x12dZOqt0aUpXqgoYQDGB\nWEHQqFFQUWLkJ0RjTDTGRBNNTLlRrzeWXMu1i8ZrjdcG9qBGVBTEBgoWkN7L0tndz++P75m6Mwvq\n7myZ9/Px2Idzzpxz5oxf5sx8zvfz/XwLSti8Odyw/aCCqQWXLk3MOdekSerwj/g2X6a2ccd2mk4g\n12LBfyyFd++9o8vuuHEAdI9uAjQoXgNlpWzcmBhP2KVL4t9MRRYuhEeeTr0gdGlfwV0EqXEUCEql\nGHpyBwynF59wDz+m06a5QCJn8LPPEmlFX32VGghm6xHcsCEaf7QxkTY4jDegd+8MAxKkKhQUwJgx\nYTzQcWe1YgBzOIknwpPr14M7r78O8+d/s+NPm0bKJONH8jINvj/s25+4VI1TT4V99mESt9OTBeFX\n5YoVWTc/5xw47TTglVdSn9D4wBqlYUMYMiSaLqRHM/ZmI2tozTpa4tu28957oTroWWd9/SAQ4ItP\nU6sCaw7B3EueQqK4GLZ7Q5Z2OISG7KQtq2jMNooKQi/e8uUhPTRb2n9yb+D++2dOH0yfTL5DJ00n\nkGvpgVx8jOaAAdCjB03ZSnuW05DtsHUr7omv4z0pErN1K1xxBWwvTny+CylhWN8NFewlNY0CQakU\ng8/owa+b38653EFHlnPTzp/QpCSR/lNWlhgGNm9eYjBygwbZU8nffDP6IoquTD1ZQFtWqzcwx/r3\nh0mTYPBZB0CLFgxiFgfwafhFEfXU3n331z/u2rXw1ptlKYWARjFN4z9rsgYNYPJkGrKT67mIi7ie\nkzbex9AhTufOid7jevXCv5mzziJ8iNMDQY0PrHHMQs9gq4M7MZDZeGyc4I7trFyyi8GD93xMYLK1\na+H591LTPjp3V1XgXCsXCG6HZa36U0gJ7VlGI7bRrGQdBQVhDrlVq2DRoszHqnDaiMjSVanzRGrq\niNxLDwTjVVvNojt0IT20kBIKtibGCTZsGO63V8Qdrr46ZHixPdED+HNupFVPVQytTRQISuUoKKDR\n9wbHfjrQgg30aZCoLd2wYaIXMHkuwbZts6eS//vf0YMozfQwZoRlBQrVo149OPJIDJjIXWHdujAW\n9O23M1ePrMiLL4IXb4ayULGsG1+wf9uNqWUMpeaZPBkKC2nKVk7gaS5c9Tv+esSL3Htv6OF94olQ\nXfL006Ptv/wyUSUKwsXg0EOr48xlNxo1grE/bUu3jjtpzZqwju3sU7aMe+/9ZoWh7r8fdqzfGl9u\nwXoGH6ZAMNdiUzTFAsGNG2F1s64UUkJHltGYbRQUF9OsWegRdIeZM8sfZ+3aRFXR+vWhe/fMr7d0\nfeo8ch17fc05heRba9UqdTklhTdKD+3BZxjQcNsGKCtl3bowgfzu5gV9+GF4/fVoIbrLP4ppjOZZ\n2G+/Sjl/yY2cBYJm1snMXjGzj83sQzP7WbT+92a2xMxmR38jk/a5zMwWmNk8Mzsmaf1AM/vAzOab\n2Q25eg+yGyNGpCz23TAj/riwEDaXn5s4a1rojh1h4mLKSuMXmfj4QAWC1eeoowA4hHfpz4chPTTy\ndXoF3WHqVFLSQkfyHDb8expkXtN16ACnnJK67vTT4YEHMJzmzdPSB9N7Aw877JvlF0pOtGsHp5xd\nxAim04wt7MNKClat4L33PFyTv4bly+GZJ3bBpkSv/wSm0OhIpX/nWosW4dJaUgLbtkXDNVq2ppAS\n+vIRe7MJiospKnJ27Ag9Q598klLQGUjtDezePQSDmSzdnJrq07HfHgw4k0rVMq1jLiU+69cP+vSJ\nF4xpymbYspUNG8IQ7orMmQO33x4tbN8OGzfQkwX8nBtDZ4CytmqVXPYIlgAXu3tf4FDgAjOLJRX8\nzd0HRn/PAZhZb2As0BsYBdxiFv+FeCsw0d33B/Y3s2Nz+D4km+HDUxb7fPYUyeMEizMUistWKOat\nt6JKV5uKwZ22rAoXrM6dM49Ml9w4+mgADDiHu0NvbdSjN3t2mGtsT8ydG5sWJASC9SjlaF5UkF9b\n/Oxnqcvr1oU80OOPj3KFCDng554LF1yQuq3SQmu83r84lusKL2cU0yjAQy5/cTF33PH1egXvvRdK\n16yLL7dnOWP6LlSPQTVo0CAUCnEPQ3s//hho3pzCekZPPmMA78GunTQo20GjRiGILyujXPCfPD7w\ngAMyv9b2NZtZW5oIBM2gXa/mlf+mpEING0K3buFxhw7lewgZNy4eCDZjM+1LvqJZs4oL+MWqRMev\nA6tWUkQxV/H7MIXYkCHQo0elvxepOjkLBN19hbvPiR5vBuZBfLbRTF0APwAecvcSd18ILAAGm1k7\noMjdYzPdTAFOrNKTlz1z0EEpA/56b3oLtiRKSG/aBLtSawZk7BGcMweuvTZaiNJCh/FG+EeiQKF6\ndesWn2H2YN5noM+Kz/EIoVdwT34oTpsG4PHxgYcxg+ZsVPvWFoceGspHpnv2WejbN9ww6NMH7ryz\nfB16FYqp+Vq3htGj+X/cmVi3YgXz58Orr+7ZIb78MqR/J48FOJt7KRyt+7bVJTbB+K5d4Uac1Sug\nXod96M7nDCKK+Io3sddeYZwghKIxTz0VMjiefz66gUcI7rJl8S97P3VG+naNN1FYX5ke1eHYY8PY\n3+OOy1DUZ9w42rCaIooxoNGmVZRu28ny5eWP4x7+HUycmJLIg61cwW/5C+2JioaNH19Vb0WqSLWM\nETSzLsDBwNvRqgvMbI6Z3WlmsUiiI7A4abel0bqOwJKk9UtIBJRSnerVS0kJKGIznS3RhA0aJMYW\nxKTfeXrjDbj00kQxmViQcSQvh2UFCtUv6hWEqFcw6YfeRx+F6UIqsn17lC24eUu8VvUopoXcpX79\nquKMpbKZwV13hTs2DdOm+9i0CV56KfN+w4ZpfGBtMWECBzCfEbHpYlauAi/jrrtCj9Lu3HUXuHvo\nLQa6sJCjeCn8IpVqEbtPG5srrrAQGuy3D51YkhQIFtOkSbisl5aG7+LZs8N4wXgBN0KnbtOmmV9n\n2Sep+aQdNW9ktWnUKKSINmmS4ckDDsAOOijeKwjA6tV89lnqZitWwC9/CddfH9KK4zZvZsKWWxlC\nNJi0Xr342EOpPXIeCJpZM+Ax4OdRz+AtQDd3PxhYAfxXrs9JKlF6eui2RFTQoEHKkDKaNUu9OE2b\nFkoRx3sNPVSUHM8/6Es0a7lyz6tfUiDYl7kMXfkk7Ez0+oQfgOV3KykJaUbXXRd9mUS3FVuyjsHM\nDG2rGYdrj8JC+NWvwuzTw3Yz5qtz5/Ar4qWX1Ma1xejR0KIF53A3BZRByS5Yu5YlS+Dxxyvede7c\naA7YTcXxC/pE7qJgr6IwRlSqRayKZHIg2OXgFhRSSkeW0pZVsH49ZuH7OXmap3QVTTa+dMHWlOWO\nrTWvXHWJ3adLv18Xl5QeCsCqVTz/PDzzTPhN9o9/hGmAMg37GNFgBj/ivsSKY46pOK9UaqSclu4y\ns0JCEHi/uz8J4O7JfUR3AE9Hj5cCyYPBOkXrsq3P6A9/+EP88YgRIxiRVtBEKllaINj3q+eY1upM\nwDBLvZuUnBb68MNw221px9q8mcllt3Aa0YzlbduqomRNcNxxofFWrgTgnJL/4a2FJ8Tb5tNPQ2HJ\nNm3CmISWLUMZ8pkzk3p6IZ72ewwvUI8y9fbWVgccAK+9BjffDL/5TWojDxkSbiWfdFL41Sm1R8OG\nMG4c+952G6OYxrOMDp/51m249dZwU+/cczPH9XfGMkrXhWyB3swLxb6OOSV7dRGpcrEJxmPZ2oWF\n0P3YnnBzGJ8ziFlMK24LO3dQVNSQrl1h5MhwE6+0NPHfli1DBng2CxakzSHYrqxq3pDsViwAzFoF\ndNw4elz+k8Tyxg3M+NcOZszIFjmGWl/nTS5jzE8npo7rOvPMb3u6UommT5/O9OnTd7tdrr+Z7wbm\nuvuNsRVm1s7dYzMSnwx8FD1+CviHmV1PSP3sAcx0dzezjWY2GHgHmADclO0FkwNByYEBA6CoKF4Z\npu/GN8IPwyYhh2TdutBbZBaq00EYe5AeBJrBLw96idGzHk6s/J4qStYITZrAlVfCT38KQE8+43vL\nH+a1fX8JjUMX74IF4S87hw0bMTykhYICwdqsoCAUkBkzBm66CXbuhDPOCL0/+szWXhMmwG238SPu\n4yWOYseaNaFnsLA+Dz0ECxeGLI5YZkdxcZg+JN57EKWNn8sd4Qfj6NHV8CYkJlZFMpaxUVgI3Ye2\nge98B955JwSCjII1a7EOHVi0qOKev0w2bIBX57YheQRP927fYN4RqRRt2oQ2yVahnW7d6NG/CXyY\ntG71KuiUuSjfwIEhEaTdJ6/B0qRRWk2bwg9+UGnnLd9eeufXVVddlXG7XE4fMQwYDxxhZu8lTRVx\nbTQVxBxgOHARgLvPBR4B5gJTgfPd4wlnPwXuAuYDC2KVRqUGKCyEww+PL3ZmEU23rk55umlTaN8+\ndBaUlpafdqCwMFSlGr12SuoTChRqjnPPTZlA6sd+F/blF3u+/9ZtNNi1mfO5hf1YHPKQBgyoghOV\nnOraNaSA3nxzSBdVEFi7DR0KPXrQhjX8lr9Q33em5Au+9Racf34YO3bNNWFWkXvuiZ7cuROKixnE\nLAYwJ6wbObL8a0jOpFeNbNkyWnfCCQAMZHZ4Ym2YQ/LTTzNX+67IU09ByZad8eX2LGfAwQoEq0vs\nqzVWKCiTrhO+S1+SJgJevBh27UzZpnFjuPjiMLSjXTvggQdSD3LSSdkHjUqNlrMeQXd/A6iX4ams\nQZy7/xX4a4b1s4D+lXd2UqmGD4+VhcSA3rs+5F26AGEscf/+MGpU2HTGjNQJ5hs0gKuvhgEHlcGJ\nr6ceV+MDa4769eHPf47PGt6FRVyw6kru7HEP2xrsnXW35s1DrZDD1rzIoJmn0ZhosMqwYUodFKlp\nzEKv4JVX8l3+zU1cyO/W3MraDon6bIsWweWXZ9g3KhITrzw6aFAiDUSqRfq8cj17RvdqTjgBrriC\nFmygG1/wxfoCKC3F69Vjzpw9/+otKYEn/89hQ6IYwEk8QcGBv6i8NyGVzsaN5S+XHshLfgTL6EDp\njnqUrnif0omTKPF6dOwY7uHEexW3b4fHHks9iKqF1lr65SWVL32c4PKXeLf9GGKzhMydmwgEn302\nddejjoo6hj6aG/8hAYRyZ/0V+9coY8eGqpFRHtjJPMHxjbay5oHnWLsW1qwJTbh2bahcNmgQ9O4d\n/fA481GIBYGg3l6RmurMM0MqONCLT7lt3an8rt3nfLoi+w0fgML1q5jI/9CLaOI5VQutdq1bpy73\nis3k3L9/KOi0aBGDmMUXZd1g/Tpo3YZZs/Y8EPzXv2Dd4s3xAkGN2cZxzV6HwQ9W3puQyrfvvuz9\nix/zw+uvT6z7HFj5KdxwQ/ntn302PvUTEOo3HHVUlZ+mVA2Vb5PKN2hQSopAn01vwtZElZiPowyE\nNWtCSlGyMWOiB6+9lvrE4YeH7kSpOQoKQvdtkvqvPE/7j16kXz8YMQJOPjlkkZ51VphWzowwQCV9\nMjIFgiI1U9euKZFAa9ZyY5frs04Huc8+MGliKY9uOz5R6AsUCNYAjRsnxnMWFCTVXjOD448HSEwj\nsSak6qRPKJ+Ne1RNdl2iN3Akz9H0iCEqEFQbXHNNuZv43Hgj3Hdf+W3/8Y/U5dNOU0ZPLaZAUCpf\n/fop5eR7My9lBtKFC0P9mKlTU6cZ6NYtukO5Y0e4ACVTWmjNdPTRcOSRqet+8xsoq6BK3MKFsCRp\nkHnDhqFYgYjUTBMmpCw2vOEafnfcbCZNCh9fszDm+69/hQcfhNO7vEnzTV8ldmjVSp/xGsAs/NZv\n0SJ8pabMLReNEzyQD6hHaTRmw1mypOJpJGLmzg1jClmfyOQ5mX+GKQWk5qtfHx59NEwQmWzyZHjn\nncTy+vXlU7lULbRWUyAoVSPpzlIRm+m8Y3582T18aUydmrrLmDFRj9Hf/gbzE9tjFv+SkhrGrFyv\nILNnw/33Z98nvbd36NAKJjkSkWp36qmhGnTM9u3YSSdy+pGr+Oc/w5Dwq68OH+WCAspf3EeOVEZH\nDXHggaG446BBaU8MHw5FRTRmeygcsmsnbAoTw+9Jr+BjjwFlpbAhpAwO4W06sTRl3lmp4dq0CWV/\nGzVKrNuxA048MYwBHDEilJHdmVRIpmdPOOSQnJ+qVB4FglI10uZr7LNoakpVmClT4tPQAaFIzFFH\nESoP/OlPqceaNCkMLpOa6ZBDwg/FZOecE3p102eWX7kSbr01dV16OoqI1Cx77w3//d+p6xYvhrFj\naVJ/V/n7OOk9BkoLrTFibZX8Wx8IX8LR4P149dAoPfTddys+5qpV0f29jRvBQzbIKTwWepd69qyk\nM5ecGDgQ7rgjdd2yZaGr/9VX4auvUp8bP17VoWs5BYJSNQYPDmNLIn35GObNDdWmgA8/TN18xIjo\nhvNFF6XOOt+qFfzHf1T9+cq385e/pI4RKCuDX/wipJXE7h4+/3y4Hf3226n7Ku1XpOabMAEuvDB1\n3auvwiWXpK5bsgQ++CCxbAbHHlv15yd7pE2bMKVA27YZnowybxLjBMM0Eq++Gu7RZvPkk9FogGh8\nYGdC0RmOPlpBQm105pnht9juFBYqLbQOUCAoVaOwMAwyjtKB+vJxqC398cfxO4bJxowh5Bc98UTq\nE9dcU77mtdQ8PXuGuePSv/TvuCP8GLjoopAelj7YpFs3FYoRqS2uu65ctgc33QT33gurV4eU0PS5\nJIYOLT+BnVSboqKQxNG8eYYnR42CevXozTz2YSVs3QLbtlFaGjqE0xM8IGQOPv10tBCNDzyZf4Ya\n4RofWHtde23F7de2LdxyS8p8wlI7mWf6ZNcRZuZ1+f3VCtdeC7/+NQ4cz9NsoSl07JSSLtK5M9xz\n63asfz/4/PPEvkOHwhtvRINOpFaYOjVUENuTWYi/+91QfWzffav+vESkcqxeHSKJ5BQxs8xRAoT5\nRn/729ycm3x73/8+TJ/OdIZzFb+HHj2gU7hG//GP5RM4nnwymmFg506Y8QbN2MyjnEoj2xn+regm\nQO21a1eYOH7FCmjfHjp0gI4dw3+bN1dvby1jZrh7uUbTL2ypWpdcAmPGhInlmRfWLV0CqxM9Q6NH\ng133n6lBYEFB6GFSEFi7HHdcmBOkW7fs2xQUwB/+AK+8oiBQpLbJVFCiohuuo0dX/TlJ5YnSQ4fz\nKgN4L54eCqEDaMeOxKZz5oR1QKgmCYzhGRqxI4w1UxBYu9WvDz/+MVx2GZx9dugh7Ns3lJ1VEFhn\n6Fe2VK2CgpAi2rlzSA+N+eQTmDeXwrnvc8ztp5QfB3jeeeGLRGqfvn3DOMBMRWD23TcMOPn97zXv\nkEhtNXAg3Hnn7rf7yU9ClUGpPaJA0IALuYmCDeuhJEwQv2IFPPRQ2OyTT0IWcLyA5Pp1FFLCSUTD\nO1QtVKRWUCAoVa9lS3j0UfoULkisKy2FlSv53qrH2Pulx+NFZIBwxzm9cqjULq1bwwsvwPnnJ3p1\nTz0V3n8fDj+8es9NRL698eNTr9OFhSFAnDw5BInz5pWvECw1X/fu0KcPAF1YxMk8DmsTcwM++CDM\nmAGXXppc181h3Xou5Cbasjqs0vhAkVpBYwQlZ4r/63ZOuCS1lPTfuJgBzEnd8J57QhqC1A1Ll4bU\nsU6dqvtMRKSyffllmDagV68McxJIrXTZZfH5YbfQhDPbvcyGXkOzb791C5NnTuQ0Hg7LTZrAunWa\nH1akBtEYQal2RRefy7ADN8eXezOPg9ODwHHjQplyqTs6dlQQKFJXde0a0j8VBNYdUXooQFO2MmnF\nVbBmddbNz+z2ZiIIhFAJWkGgSK2gQTqSO2ZcMn0M3f/8EaWr13Hyoa2wNo9C48bhr0MHOOAADUIW\nERGpLkOGhDlfo/kgR/IcT3/2DPP2Gh8mnk9y8slwznM3pO6v8YEitYZSQ0VEREQkYdasMIVTSQkA\nn3AA57d+BO/Xn1BKBo49Fn590U6sVUvYsiWx74cfQr9+1XDSIpKNUkNFREREZPcGDYIrr4wv9uJT\nfrTmv0LpUOCoo+BXvwJ7+63UILB9+1A5WkRqBfUIioiIiEiqkhIYNgxmzoyv+qJpf0qensb+3+8Y\n1l9+Obz8cmKfs86CKVOq4WRFpCLZegQVCIqIiIhIefPnh2JAibkiwjQhBQXw7rvlt58yJQSDIlKj\nKBAUERERka/n5pvhggt2v13jxrBoUZgLWERqFI0RFBEREZGv57zzdl8JtEsXeOQRBYEitYx6BEVE\nREQkuyVLoH9/2LAhsc4MRo2C88+HkSOhXr3qOz8RqZBSQ0VERETkm3n7bZg0CXbuhOOPh8mToXv3\n6j4rEdkDCgRFRERERETyjMYIioiIiIiICKBAUEREREREJO8oEBQREREREckzCgRFRERERETyjAJB\nERERERGRPKNAUEREREREJM8oEBQREREREckzCgRFRERERETyjAJBERERERGRPKNAUEREREREJM8o\nEBQREREREckzCgRFRERERETyjAJBERERERGRPKNAUEREREREJM8oEBQREREREckzCgRFRERERETy\njAJBERERERGRPKNAUEREREREJM8oEBQREREREckzCgRFRERERETyjAJBERERERGRPKNAUERERERE\nJM8oEBQREREREckzCgRFRERERETyjAJBERERERGRPKNAUEREREREJM8oEBQREREREckzCgRFRERE\nRETyjAJBERERERGRPKNAUEREREREJM8oEBQREREREckzCgRFRERERETyjAJBERERERGRPKNAUERE\nREREJM8oEBQREREREckzCgRFRERERETyjAJBERERERGRPKNAUEREREREJM8oEBQREREREckzCgRF\nRERERETyjAJBERERERGRPKNAUEREREREJM8oEBQREREREckzCgRFRERERETyjAJBERERERGRPKNA\nUEREREREJM8oEBQREREREckzCgRFRERERETyjAJBERERERGRPKNAUEREREREJM8oEBQREREREckz\nCgRFRERERETyjAJBERERERGRPKNAUEREREREJM8oEBQREREREckzCgRFRERERETyjAJBERERERGR\nPKNAUEREREREJM/kLBA0s05m9oqZfWxmH5rZhdH6Fmb2gpl9ambPm9neSftcZmYLzGyemR2TtH6g\nmX1gZvPN7IZcvQf5ZqZPn17dpyA5pPbOL2rv/KL2zi9q7/yi9s4/uewRLAEudve+wKHAT82sF/Ab\n4CV3PwB4BbgMwMz6AGOB3sAo4BYzswOB/HAAAArESURBVOhYtwIT3X1/YH8zOzaH70O+Jl1Y8ova\nO7+ovfOL2ju/qL3zi9o7/+QsEHT3Fe4+J3q8GZgHdAJ+ANwXbXYfcGL0+ATgIXcvcfeFwAJgsJm1\nA4rc/Z1ouylJ+4iIiIiIiMhuVMsYQTPrAhwMvAXs4+4rIQSLQNtos47A4qTdlkbrOgJLktYvidaJ\niIiIiIjIHjB3z+0LmjUDpgN/cvcnzWydu7dMen6tu7cys78Db7r7g9H6O4GpwCLgr+5+TLT+cOBS\ndz8hw2vl9s2JiIiIiIjUMO5u6esKc3kCZlYIPAbc7+5PRqtXmtk+7r4ySvtcFa1fCuybtHunaF22\n9eVkesMiIiIiIiL5LtepoXcDc939xqR1TwFnR49/BDyZtP40M2tgZl2BHsDMKH10o5kNjorHTEja\nR0RERERERHYjZ6mhZjYMeA34EPDo73JgJvAIoZdvETDW3TdE+1wGTAR2AT939xei9YOAe4FGwFR3\n/3lO3oSIiIiIiEgdkPMxgiIiIiIiIlK9qqVqqOSemZWZ2X8mLf/SzK6shOM2MLOHzGyBmb1pZvtF\n6w8ysxlm9qGZzTGzsVn2v9bM5kXbPG5meyU9d1l03HlmdkzS+j+b2VdmtinLMX8Yvd+B3/b91VbV\n0N77mdksM5sdtfnkLPurvatArts76fkiM1tsZjdl2V/tXQWqo73NrDT6fL9nZv+XZX+1dxWopvbe\n18yeN7O5ZvZR+mc/2kbtXQWq4ft7RPS5jn2+t5lZpgKMau86SIFg/tgBnGxmLXe75dczEVjn7j2B\nG4Bro/VbgbPcvT8wCrgh+aKR5AWgr7sfTJgr8jIAM+sDjAV6R/vfYmax4j9PAd/JdDIWqtJeSJia\nJJ/lur2XA0PdfSAwBPiNheJP6dTeVSPX7R3zJ+DVCvZXe1eN6mjvLe4+0N0HuHu2uXvV3lWjOtp7\nCnCNu/cBBpMo5JdM7V01ctre7j49+lwPBI4AthDaNp3auw5SIJg/SoDbgYvTnzCzzmb2cnSX50Uz\n62Rme5nZwqRtmkR3deql7f4D4L7o8WPAkQDuvsDdP48eLyd8ibRJf213f8ndy6LFtwhVYAFOAB5y\n9xJ3X0i46AyO9pkZm3sygz8BVxMupPks1+29y913ResbAxkr9qq9q0xO2zvaZxBh3tdMPxgAtXcV\nynl7k+UznUztXWVy2t5m1huo5+6vALj7Vnffnv7aau8qUx2f75hTgGlq7/yhQDB/OHAzMN7MitKe\n+ztwT3SX50Hg7+6+CXjPzIZH24wBnnP30rR9OwKLAaLnNqTfxTKzwUD9WGBYgXMIc0WmHDeyNFqX\nlZkNADq5+7TdvE4+yHl7R19I7xOKPl0TVfitiNq78uS0vaO7vdcBl7AHAUJE7V15quN63tDM3rWQ\n8v+DPThHtXflyXV770+ozv64hZT/a5J6eLJRe1eeavu9BpwG/O8enKPau45QIJhH3H0z4W5QepXV\nQ0l88O8HhkWPHwHGRY9PAx7eg5dJ+bIws/aEFJOzK9zJ7LfALnffkwtQpv0N+Bvwy2znkm9y3d7u\nvsTdDyJM9XK2mZXrAY7vpPaudDlu7/OBZ919Wdr6zDupvStdNVzPO7v7IcB4Qqp/16w7qb0rXY7b\nuxA4nNAj9R2gOxV8h6u9K181/V5rB/QDnq9wJ7V3naJAMP/cSMgTb5q0Llvp2KeAkWbWAhgIvJJh\nmyWEqT+I0hD2cvd10XIR8Axwmbu/k+2EzOxs4DjgjKTVS2PHjXSK1mVTBPQFppvZl8BQ4EnTAOSc\ntXf84KEn8CPgu5leRO1dpXLV3ocCF5jZF4SewbPM7D8yvYjau0rl7PMdpfjj7l8C04EBmV5E7V2l\nctXeS4A57r4oSgX8v+gY5ai9q1Suv7/HAk9k6EmMU3vXPQoE84cBuPt6wp2jiUnPzQBOjx6fCbwe\nbbsFeJdwMXrGPeNcI08DP4oen0p08TGz+oQvj/vc/YmsJ2U2EvgVcIK7J+eJPwWcZqHKVVdCL9PM\nTO8pOtdN7t7W3bu5e1dC/vrx7j4722vXcblu745m1ih63IJwN/nTciel9q4qOW1vdz/T3bu4ezdC\neugUd7+83EmpvatKrj/fzc2sQfS4NXAYMLfcSam9q0pO2xt4B2huZq2i5SNQe+dSrts75nQqSAtV\ne9dR7q6/PPgDNiU9bgtsBq6IlvcDXgbmAC8S8rZj2/4QKAUOz3LchoQL1QLCh7lLtH48YQDwbOC9\n6L8HZth/AWFM2ezo75ak5y4DPgPmAcckrb+GkI9eAnwFXJnhuK8AA6v7/3setfdRwPtRW88BJmbZ\nX+1dB9o7bZsfATepvetuexN6gD+IPt/vA2ervetue0fPHRm19fvA3UCh2rtOt3dnYPFuzkvtXQf/\nNKG8iIiIiIhInlFqqIiIiIiISJ5RICgiIiIiIpJnFAiKiIiIiIjkGQWCIiIiIiIieUaBoIiIiIiI\nSJ5RICgiIiIiIpJnFAiKiEjeMLN9zWyTmdnutxYREam7FAiKiEidZmZfmtkRAO6+2N338hxOomtm\nw81sca5eT0REZE8oEBQREalaBuQs8BQREdkTCgRFRKTOMrMpwH7AM1FK6K/MrMzMCqLn/2VmfzKz\nN8ys2MyeNLOWZvaAmW00s7fNbL+k4/UysxfMbK2ZzTOzU5OeO87MPo5eZ7GZXWxmTYCpQIfo+JvM\nrJ2ZfcfMZpjZejNbamZ/N7PCpGOVmdl5ZjY/Oo8/mlm36Dw3mNlDse1jPY5mdpmZrTazL8zsjFz9\nPxYRkdpJgaCIiNRZ7j4B+AoY7e57AY9QvnduHDAe6AD0AGYAdwEtgE+A3wNEQd0LwANAa+A04BYz\n6xUd507g3Oh1+gGvuPtWYBSwzN2LorTUFUAp8AugJXAocARwftp5HQMMAIYClwL/A5wB7Av0B05P\n2rZddKwOwNnA7WbW82v+7xIRkTyiQFBERPJBRcVh7nH3he5eDEwDPnf3f7l7GfAoIRgDGAN86e5T\nPHgfeByI9QruBPqaWZG7b3T3Odle0N1nu/vM6DhfAbcDw9M2u8bdt7j7POAj4AV3X5R0ngOSDwlc\n4e673P014Flg7O7/t4iISL5SICgiIvluZdLjbRmWm0WPOwNDzWxd9Lee0EO3T/T8D4HRwKIo5XRo\nthc0s55m9rSZLTezDcBfCL2MyVbt4XkBrHf37UnLiwi9gyIiIhkpEBQRkbqusgq1LAamu3vL6K9F\nlOp5AYC7z3L3E4E2wJOENNRsr38rMA/o7u7Ngd9Sca/l7rQws8ZJy/sBy77F8UREpI5TICgiInXd\nCqBb9Nj45gHXM8D+ZnammRWaWX0zOyQqIFPfzM4ws73cvRQoJowDhNCT18rM9ko6VhGwyd23RmMM\nz/uG5xRjwFXReXyX0DP56Lc8poiI1GEKBEVEpK67GrjCzNYR0jeTe+j2uLfQ3TcTCricRuhtWxYd\nu0G0yVnAl1Gq5yRCARrc/VPgf4EvopTSdsAlwHgz20QoAvNQ+svtZjndcmB9dE73A5Pdff6evjcR\nEck/lsM5dUVERKSSmdlw4H5332+3G4uIiETUIygiIiIiIpJnFAiKiIiIiIjkGaWGioiIiIiI5Bn1\nCIqIiIiIiOQZBYIiIiIiIiJ5RoGgiIiIiIhInlEgKCIiIiIikmcUCIqIiIiIiOSZ/w9zCWQUxcHu\nDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf0c27e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+1')][['timestamp', 'actual']]\n",
    "for t in range(1, HORIZON+1):\n",
    "    plot_df['t+'+str(t)] = eval_df[(eval_df.timestamp<'2014-11-08') & (eval_df.h=='t+'+str(t))]['prediction'].values\n",
    "\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+1'], color='blue', linewidth=4.0, alpha=0.75)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+2'], color='blue', linewidth=3.0, alpha=0.5)\n",
    "ax.plot(plot_df['timestamp'], plot_df['t+3'], color='blue', linewidth=2.0, alpha=0.25)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "ax.legend(loc='best')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
